Computer architecture for emulating a correlithm object processing system that uses portions of correlithm objects in a distributed node network

ABSTRACT

A distributed node network to emulate a correlithm object processing system, includes a resolution node, a first calculation node, and a second calculation node. The first and second calculation nodes determine the n-dimensional distances between an input correlithm object and first and second portions of each source correlithm object of a mapping table, respectively. The resolution node adds the n-dimensional distance determined for the first portion of each source correlithm object with the n-dimensional distance determined for the second portion of each source correlithm object. It compares these determined n-dimensional distances to each other to identify the source correlithm object with the smallest n-dimensional distance to the input correlithm object. It identifies the target correlithm object that corresponds to the identified source correlithm object, and outputs that target correlithm object.

TECHNICAL FIELD

The present disclosure relates generally to computer architectures foremulating a processing system, and more specifically to computerarchitectures for emulating a correlithm object processing system.

BACKGROUND

Conventional computers are highly attuned to using operations thatrequire manipulating ordinal numbers, especially ordinal binaryintegers. The value of an ordinal number corresponds with its positionin a set of sequentially ordered number values. These computers useordinal binary integers to represent, manipulate, and store information.These computers rely on the numerical order of ordinal binary integersrepresenting data to perform various operations such as counting,sorting, indexing, and mathematical calculations. Even when performingoperations that involve other number systems (e.g. floating point),conventional computers still resort to using ordinal binary integers toperform any operations.

Ordinal based number systems only provide information about the sequenceorder of the numbers themselves based on their numeric values. Ordinalnumbers do not provide any information about any other types ofrelationships for the data being represented by the numeric values suchas similarity. For example, when a conventional computer uses ordinalnumbers to represent data samples (e.g. images or audio signals),different data samples are represented by different numeric values. Thedifferent numeric values do not provide any information about howsimilar or dissimilar one data sample is from another. Unless there isan exact match in ordinal number values, conventional systems are unableto tell if a data sample matches or is similar to any other datasamples. As a result, conventional computers are unable to use ordinalnumbers by themselves for comparing different data samples and insteadthese computers rely on complex signal processing techniques.Determining whether a data sample matches or is similar to other datasamples is not a trivial task and poses several technical challenges forconventional computers. These technical challenges result in complexprocesses that consume processing power which reduces the speed andperformance of the system. The ability to compare unknown data samplesto known data samples is crucial for many security applications such asface recognition, voice recognition, and fraud detection.

Thus, it is desirable to provide a solution that allows computingsystems to efficiently determine how similar different data samples areto each other and to perform operations based on their similarity.

SUMMARY

Conventional computers are highly attuned to using operations thatrequire manipulating ordinal numbers, especially ordinal binaryintegers. The value of an ordinal number corresponds with its positionin a set of sequentially ordered number values. These computers useordinal binary integers to represent, manipulate, and store information.These computers rely on the numerical order of ordinal binary integersrepresenting data to perform various operations such as counting,sorting, indexing, and mathematical calculations. Even when performingoperations that involve other number systems (e.g. floating point),conventional computers still resort to using ordinal binary integers toperform any operations.

Ordinal based number systems only provide information about the sequenceorder of the numbers themselves based on their numeric values. Ordinalnumbers do not provide any information about any other types ofrelationships for the data being represented by the numeric values suchas similarity. For example, when a conventional computer uses ordinalnumbers to represent data samples (e.g. images or audio signals),different data samples are represented by different numeric values. Thedifferent numeric values do not provide any information about howsimilar or dissimilar one data sample is from another. Unless there isan exact match in ordinal number values, conventional systems are unableto tell if a data sample matches or is similar to any other datasamples. As a result, conventional computers are unable to use ordinalnumbers by themselves for comparing different data samples and insteadthese computers rely on complex signal processing techniques.Determining whether a data sample matches or is similar to other datasamples is not a trivial task and poses several technical challenges forconventional computers. These technical challenges result in complexprocesses that consume processing power which reduces the speed andperformance of the system. The ability to compare unknown data samplesto known data samples is crucial for many applications such as securityapplication (e.g. face recognition, voice recognition, and frauddetection).

The system described in the present application provides a technicalsolution that enables the system to efficiently determine how similardifferent objects are to each other and to perform operations based ontheir similarity. In contrast to conventional systems, the system usesan unconventional configuration to perform various operations usingcategorical numbers and geometric objects, also referred to ascorrelithm objects, instead of ordinal numbers. Using categoricalnumbers and correlithm objects on a conventional device involveschanging the traditional operation of the computer to supportrepresenting and manipulating concepts as correlithm objects. A deviceor system may be configured to implement or emulate a special purposecomputing device capable of performing operations using correlithmobjects. Implementing or emulating a correlithm object processing systemimproves the operation of a device by enabling the device to performnon-binary comparisons (i.e. match or no match) between different datasamples. This enables the device to quantify a degree of similaritybetween different data samples. This increases the flexibility of thedevice to work with data samples having different data types and/orformats, and also increases the speed and performance of the device whenperforming operations using data samples. These technical advantages andother improvements to the device are described in more detail throughoutthe disclosure.

In one embodiment, the system is configured to use binary integers ascategorical numbers rather than ordinal numbers which enables the systemto determine how similar a data sample is to other data samples.Categorical numbers provide information about similar or dissimilardifferent data samples are from each other. For example, categoricalnumbers can be used in facial recognition applications to representdifferent images of faces and/or features of the faces. The systemprovides a technical advantage by allowing the system to assigncorrelithm objects represented by categorical numbers to different datasamples based on how similar they are to other data samples. As anexample, the system is able to assign correlithm objects to differentimages of people such that the correlithm objects can be directly usedto determine how similar the people in the images are to each other. Inother words, the system is able to use correlithm objects in facialrecognition applications to quickly determine whether a captured imageof a person matches any previously stored images without relying onconventional signal processing techniques.

Correlithm object processing systems use new types of data structurescalled correlithm objects that improve the way a device operates, forexample, by enabling the device to perform non-binary data setcomparisons and to quantify the similarity between different datasamples. Correlithm objects are data structures designed to improve theway a device stores, retrieves, and compares data samples in memory.Correlithm objects also provide a data structure that is independent ofthe data type and format of the data samples they represent. Correlithmobjects allow data samples to be directly compared regardless of theiroriginal data type and/or format.

A correlithm object processing system uses a combination of a sensortable, a node table, and/or an actor table to provide a specific set ofrules that improve computer-related technologies by enabling devices tocompare and to determine the degree of similarity between different datasamples regardless of the data type and/or format of the data samplethey represent. The ability to directly compare data samples havingdifferent data types and/or formatting is a new functionality thatcannot be performed using conventional computing systems and datastructures.

In addition, correlithm object processing system uses a combination of asensor table, a node table, and/or an actor table to provide aparticular manner for transforming data samples between ordinal numberrepresentations and correlithm objects in a correlithm object domain.Transforming data samples between ordinal number representations andcorrelithm objects involves fundamentally changing the data type of datasamples between an ordinal number system and a categorical number systemto achieve the previously described benefits of the correlithm objectprocessing system.

Using correlithm objects allows the system or device to compare datasamples (e.g. images) even when the input data sample does not exactlymatch any known or previously stored input values. For example, an inputdata sample that is an image may have different lighting conditions thanthe previously stored images. The differences in lighting conditions canmake images of the same person appear different from each other. Thedevice uses an unconventional configuration that implements a correlithmobject processing system that uses the distance between the data sampleswhich are represented as correlithm objects and other known data samplesto determine whether the input data sample matches or is similar to theother known data samples. Implementing a correlithm object processingsystem fundamentally changes the device and the traditional dataprocessing paradigm. Implementing the correlithm object processingsystem improves the operation of the device by enabling the device toperform non-binary comparisons of data samples. In other words, thedevice is able to determine how similar the data samples are to eachother even when the data samples are not exact matches. In addition, thedevice is able to quantify how similar data samples are to one another.The ability to determine how similar data samples are to each other isunique and distinct from conventional computers that can only performbinary comparisons to identify exact matches.

The problems associated with comparing data sets and identifying matchesbased on the comparison are problems necessarily rooted in computertechnologies. As described above, conventional systems are limited to abinary comparison that can only determine whether an exact match isfound. Emulating a correlithm object processing system provides atechnical solution that addresses problems associated with comparingdata sets and identifying matches. Using correlithm objects to representdata samples fundamentally changes the operation of a device and how thedevice views data samples. By implementing a correlithm objectprocessing system, the device can determine the distance between thedata samples and other known data samples to determine whether the inputdata sample matches or is similar to the other known data samples. Inaddition, the device is able to determine a degree of similarity thatquantifies how similar different data samples are to one another.

In one embodiment, a node is generally configured to receive an inputcorrelithm object, identify the source correlithm object that mostclosely matches the input correlithm object, and output the targetcorrelithm object corresponding to the identified source correlithmobject. Finding the source correlithm object that most closely matchesthe input correlithm object may involve computing the n-dimensionaldistance (e.g., Hamming distance, Minkowski distance, or other suitabledistance) between the input correlithm object and each of the sourcecorrelithm objects, and identifying the source correlithm object thatresults in the smallest n-dimensional distance calculation. Performingthis distance calculation serially for a relatively large number ofsource correlithm objects in a relatively large correlithm objectmapping table can be time and resource consuming, which can createbottlenecks in many aspects of a correlithm object processing system.The present disclosure provides a technical solution whereby then-dimensional distance calculation is performed using parallelprocessing techniques implemented by calculation nodes, and a resolutionnode, as described in greater detail below.

The system is configured to implement parallel processing using groupsof nodes that can execute simultaneously on different processors orservers associated with the same or different computers. In this way,the described systems can leverage multi-core, multi-threaded andmulti-processor computers having multiple processing elements within asingle machine; cluster computing (composed of multiple standalonemachines connected by a network); massively parallel processors (asingle computer with many networked processors); symmetricmultiprocessors (a computer system with multiple identical processorsthat share memory and connect via a bus); and grid computing (computerscommunicating over the Internet to work on a given problem), amongothers. Performing parallel processing increases the speed andefficiency of calculating n-dimensional distances between inputcorrelithm objects and source correlithm objects of a mapping table,among other tasks. This reduces bottlenecks in the network and in theoverall correlithm object processing systems. This increases thefunctionality of the devices that implement the correlithm objectprocessing systems, and thereby improves their operation.

Certain embodiments of the present disclosure may include some, all, ornone of these advantages. These advantages and other features will bemore clearly understood from the following detailed description taken inconjunction with the accompanying drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure, reference is nowmade to the following brief description, taken in connection with theaccompanying drawings and detailed description, wherein like referencenumerals represent like parts.

FIG. 1 is a schematic view of an embodiment of a special purposecomputer implementing correlithm objects in an n-dimensional space;

FIG. 2 is a perspective view of an embodiment of a mapping betweencorrelithm objects in different n-dimensional spaces;

FIG. 3 is a schematic view of an embodiment of a correlithm objectprocessing system;

FIG. 4 is a protocol diagram of an embodiment of a correlithm objectprocess flow;

FIG. 5 is a schematic diagram of an embodiment a computer architecturefor emulating a correlithm object processing system;

FIGS. 6A-B illustrate a schematic diagram of one embodiment of acorrelithm object processing system;

FIG. 7 illustrates one embodiment of a flowchart implementing a processperformed by the components of the correlithm object processing systemillustrated in FIGS. 6A-B;

FIGS. 8A-B illustrate a schematic diagram of another embodiment of acorrelithm object processing system;

FIGS. 9A-B illustrate a schematic diagram of another embodiment of acorrelithm object processing system;

FIG. 10 illustrates one embodiment of a flowchart implementing a processperformed by the components of the correlithm object processing systemillustrated in FIGS. 8A-B;

FIG. 11 illustrates one embodiment of a flowchart implementing a processperformed by the components of the correlithm object processing systemillustrated in FIGS. 9A-B;

FIGS. 12A-B illustrate a schematic diagram of one embodiment of acorrelithm object processing system; and

FIGS. 13A-B illustrates one embodiment of a flowchart implementing aprocess performed by the components of the correlithm object processingsystem illustrated in FIGS. 12A-B.

DETAILED DESCRIPTION

FIGS. 1-5 describe various embodiments of how a correlithm objectprocessing system may be implemented or emulated in hardware, such as aspecial purpose computer. FIG. 1 is a schematic view of an embodiment ofa user device 100 implementing correlithm objects 104 in ann-dimensional space 102. Examples of user devices 100 include, but arenot limited to, desktop computers, mobile phones, tablet computers,laptop computers, or other special purpose computer platform. The userdevice 100 is configured to implement or emulate a correlithm objectprocessing system that uses categorical numbers to represent datasamples as correlithm objects 104 in a high-dimensional space 102, forexample a high-dimensional binary cube. Additional information about thecorrelithm object processing system is described in FIG. 3. Additionalinformation about configuring the user device 100 to implement oremulate a correlithm object processing system is described in FIG. 5.

Conventional computers rely on the numerical order of ordinal binaryintegers representing data to perform various operations such ascounting, sorting, indexing, and mathematical calculations. Even whenperforming operations that involve other number systems (e.g. floatingpoint), conventional computers still resort to using ordinal binaryintegers to perform any operations. Ordinal based number systems onlyprovide information about the sequence order of the numbers themselvesbased on their numeric values. Ordinal numbers do not provide anyinformation about any other types of relationships for the data beingrepresented by the numeric values, such as similarity. For example, whena conventional computer uses ordinal numbers to represent data samples(e.g. images or audio signals), different data samples are representedby different numeric values. The different numeric values do not provideany information about how similar or dissimilar one data sample is fromanother. In other words, conventional computers are only able to makebinary comparisons of data samples which only results in determiningwhether the data samples match or do not match. Unless there is an exactmatch in ordinal number values, conventional systems are unable to tellif a data sample matches or is similar to any other data samples. As aresult, conventional computers are unable to use ordinal numbers bythemselves for determining similarity between different data samples,and instead these computers rely on complex signal processingtechniques. Determining whether a data sample matches or is similar toother data samples is not a trivial task and poses several technicalchallenges for conventional computers. These technical challenges resultin complex processes that consume processing power which reduces thespeed and performance of the system.

In contrast to conventional systems, the user device 100 operates as aspecial purpose machine for implementing or emulating a correlithmobject processing system. Implementing or emulating a correlithm objectprocessing system improves the operation of the user device 100 byenabling the user device 100 to perform non-binary comparisons (i.e.match or no match) between different data samples. This enables the userdevice 100 to quantify a degree of similarity between different datasamples. This increases the flexibility of the user device 100 to workwith data samples having different data types and/or formats, and alsoincreases the speed and performance of the user device 100 whenperforming operations using data samples. These improvements and otherbenefits to the user device 100 are described in more detail below andthroughout the disclosure.

For example, the user device 100 employs the correlithm objectprocessing system to allow the user device 100 to compare data sampleseven when the input data sample does not exactly match any known orpreviously stored input values. Implementing a correlithm objectprocessing system fundamentally changes the user device 100 and thetraditional data processing paradigm. Implementing the correlithm objectprocessing system improves the operation of the user device 100 byenabling the user device 100 to perform non-binary comparisons of datasamples. In other words, the user device 100 is able to determine howsimilar the data samples are to each other even when the data samplesare not exact matches. In addition, the user device 100 is able toquantify how similar data samples are to one another. The ability todetermine how similar data samples are to each others is unique anddistinct from conventional computers that can only perform binarycomparisons to identify exact matches.

The user device's 100 ability to perform non-binary comparisons of datasamples also fundamentally changes traditional data searching paradigms.For example, conventional search engines rely on finding exact matchesor exact partial matches of search tokens to identify related datasamples. For instance, conventional text-based search engines arelimited to finding related data samples that have text that exactlymatches other data samples. These search engines only provide a binaryresult that identifies whether or not an exact match was found based onthe search token. Implementing the correlithm object processing systemimproves the operation of the user device 100 by enabling the userdevice 100 to identify related data samples based on how similar thesearch token is to other data sample. These improvements result inincreased flexibility and faster search time when using a correlithmobject processing system. The ability to identify similarities betweendata samples expands the capabilities of a search engine to include datasamples that may not have an exact match with a search token but arestill related and similar in some aspects. The user device 100 is alsoable to quantify how similar data samples are to each other based oncharacteristics besides exact matches to the search token. Implementingthe correlithm object processing system involves operating the userdevice 100 in an unconventional manner to achieve these technologicalimprovements as well as other benefits described below for the userdevice 100.

Computing devices typically rely on the ability to compare data sets(e.g. data samples) to one another for processing. For example, insecurity or authentication applications a computing device is configuredto compare an input of an unknown person to a data set of known people(or biometric information associated with these people). The problemsassociated with comparing data sets and identifying matches based on thecomparison are problems necessarily rooted in computer technologies. Asdescribed above, conventional systems are limited to a binary comparisonthat can only determine whether an exact match is found. As an example,an input data sample that is an image of a person may have differentlighting conditions than previously stored images. In this example,different lighting conditions can make images of the same person appeardifferent from each other. Conventional computers are unable todistinguish between two images of the same person with differentlighting conditions and two images of two different people withoutcomplicated signal processing. In both of these cases, conventionalcomputers can only determine that the images are different. This isbecause conventional computers rely on manipulating ordinal numbers forprocessing.

In contrast, the user device 100 uses an unconventional configurationthat uses correlithm objects to represent data samples. Using correlithmobjects to represent data samples fundamentally changes the operation ofthe user device 100 and how the device views data samples. Byimplementing a correlithm object processing system, the user device 100can determine the distance between the data samples and other known datasamples to determine whether the input data sample matches or is similarto the other known data samples, as explained in detail below. Unlikethe conventional computers described in the previous example, the userdevice 100 is able to distinguish between two images of the same personwith different lighting conditions and two images of two differentpeople by using correlithm objects 104. Correlithm objects allow theuser device 100 to determine whether there are any similarities betweendata samples, such as between two images that are different from eachother in some respects but similar in other respects. For example, theuser device 100 is able to determine that despite different lightingconditions, the same person is present in both images.

In addition, the user device 100 is able to determine a degree ofsimilarity that quantifies how similar different data samples are to oneanother. Implementing a correlithm object processing system in the userdevice 100 improves the operation of the user device 100 when comparingdata sets and identifying matches by allowing the user device 100 toperform non-binary comparisons between data sets and to quantify thesimilarity between different data samples. In addition, using acorrelithm object processing system results in increased flexibility andfaster search times when comparing data samples or data sets. Thus,implementing a correlithm object processing system in the user device100 provides a technical solution to a problem necessarily rooted incomputer technologies.

The ability to implement a correlithm object processing system providesa technical advantage by allowing the system to identify and comparedata samples regardless of whether an exact match has been previousobserved or stored. In other words, using the correlithm objectprocessing system the user device 100 is able to identify similar datasamples to an input data sample in the absence of an exact match. Thisfunctionality is unique and distinct from conventional computers thatcan only identify data samples with exact matches.

Examples of data samples include, but are not limited to, images, files,text, audio signals, biometric signals, electric signals, or any othersuitable type of data. A correlithm object 104 is a point in then-dimensional space 102, sometimes called an “n-space.” The value ofrepresents the number of dimensions of the space. For example, ann-dimensional space 102 may be a 3-dimensional space, a 50-dimensionalspace, a 100-dimensional space, or any other suitable dimension space.The number of dimensions depends on its ability to support certainstatistical tests, such as the distances between pairs of randomlychosen points in the space approximating a normal distribution. In someembodiments, increasing the number of dimensions in the n-dimensionalspace 102 modifies the statistical properties of the system to provideimproved results. Increasing the number of dimensions increases theprobability that a correlithm object 104 is similar to other adjacentcorrelithm objects 104. In other words, increasing the number ofdimensions increases the correlation between how close a pair ofcorrelithm objects 104 are to each other and how similar the correlithmobjects 104 are to each other.

Correlithm object processing systems use new types of data structurescalled correlithm objects 104 that improve the way a device operates,for example, by enabling the device to perform non-binary data setcomparisons and to quantify the similarity between different datasamples. Correlithm objects 104 are data structures designed to improvethe way a device stores, retrieves, and compares data samples in memory.Unlike conventional data structures, correlithm objects 104 are datastructures where objects can be expressed in a high-dimensional spacesuch that distance 106 between points in the space represent thesimilarity between different objects or data samples. In other words,the distance 106 between a pair of correlithm objects 104 in then-dimensional space 102 indicates how similar the correlithm objects 104are from each other and the data samples they represent. Correlithmobjects 104 that are close to each other are more similar to each otherthan correlithm objects 104 that are further apart from each other. Forexample, in a facial recognition application, correlithm objects 104used to represent images of different types of glasses may be relativelyclose to each other compared to correlithm objects 104 used to representimages of other features such as facial hair. An exact match between twodata samples occurs when their corresponding correlithm objects 104 arethe same or have no distance between them. When two data samples are notexact matches but are similar, the distance between their correlithmobjects 104 can be used to indicate their similarities. In other words,the distance 106 between correlithm objects 104 can be used to identifyboth data samples that exactly match each other as well as data samplesthat do not match but are similar. This feature is unique to acorrelithm processing system and is unlike conventional computers thatare unable to detect when data samples are different but similar in someaspects.

Correlithm objects 104 also provide a data structure that is independentof the data type and format of the data samples they represent.Correlithm objects 104 allow data samples to be directly comparedregardless of their original data type and/or format. In some instances,comparing data samples as correlithm objects 104 is computationally moreefficient and faster than comparing data samples in their originalformat. For example, comparing images using conventional data structuresinvolves significant amounts of image processing which is time consumingand consumes processing resources. Thus, using correlithm objects 104 torepresent data samples provides increased flexibility and improvedperformance compared to using other conventional data structures.

In one embodiment, correlithm objects 104 may be represented usingcategorical binary strings. The number of bits used to represent thecorrelithm object 104 corresponds with the number of dimensions of then-dimensional space 102 where the correlithm object 102 is located. Forexample, each correlithm object 104 may be uniquely identified using a64-bit string in a 64-dimensional space 102. As another example, eachcorrelithm object 104 may be uniquely identified using a 10-bit stringin a 10-dimensional space 102. In other examples, correlithm objects 104can be identified using any other suitable number of bits in a stringthat corresponds with the number of dimensions in the n-dimensionalspace 102.

In this configuration, the distance 106 between two correlithm objects104 can be determined based on the differences between the bits of thetwo correlithm objects 104. In other words, the distance 106 between twocorrelithm objects can be determined based on how many individual bitsdiffer between the correlithm objects 104. The distance 106 between twocorrelithm objects 104 can be computed using Hamming distance or anyother suitable technique.

As an example using a 10-dimensional space 102, a first correlithmobject 104 is represented by a first 10-bit string (1001011011) and asecond correlithm object 104 is represented by a second 10-bit string(1000011011). The Hamming distance corresponds with the number of bitsthat differ between the first correlithm object 104 and the secondcorrelithm object 104. In other words, the Hamming distance between thefirst correlithm object 104 and the second correlithm object 104 can becomputed as follows:

1001011011 1000011011 ----------------- 0001000000In this example, the Hamming distance is equal to one because only onebit differs between the first correlithm object 104 and the secondcorrelithm object. As another example, a third correlithm object 104 isrepresented by a third 10-bit string (0110100100). In this example, theHamming distance between the first correlithm object 104 and the thirdcorrelithm object 104 can be computed as follows:

1001011011 0110100100 ----------------- 1111111111The Hamming distance is equal to ten because all of the bits aredifferent between the first correlithm object 104 and the thirdcorrelithm object 104. In the previous example, a Hamming distance equalto one indicates that the first correlithm object 104 and the secondcorrelithm object 104 are close to each other in the n-dimensional space102, which means they are similar to each other. In the second example,a Hamming distance equal to ten indicates that the first correlithmobject 104 and the third correlithm object 104 are further from eachother in the n-dimensional space 102 and are less similar to each otherthan the first correlithm object 104 and the second correlithm object104. In other words, the similarity between a pair of correlithm objectscan be readily determined based on the distance between the paircorrelithm objects.

As another example, the distance between a pair of correlithm objects104 can be determined by performing an XOR operation between the pair ofcorrelithm objects 104 and counting the number of logical high values inthe binary string. The number of logical high values indicates thenumber of bits that are different between the pair of correlithm objects104 which also corresponds with the Hamming distance between the pair ofcorrelithm objects 104.

In another embodiment, the distance 106 between two correlithm objects104 can be determined using a Minkowski distance such as the Euclideanor “straight-line” distance between the correlithm objects 104. Forexample, the distance 106 between a pair of correlithm objects 104 maybe determined by calculating the square root of the sum of squares ofthe coordinate difference in each dimension.

The user device 100 is configured to implement or emulate a correlithmobject processing system that comprises one or more sensors 302, nodes304, and/or actors 306 in order to convert data samples between realworld values or representations and to correlithm objects 104 in acorrelithm object domain. Sensors 302 are generally configured toconvert real world data samples to the correlithm object domain. Nodes304 are generally configured to process or perform various operations oncorrelithm objects in the correlithm object domain. Actors 306 aregenerally configured to convert correlithm objects 104 into real worldvalues or representations. Additional information about sensors 302,nodes 304, and actors 306 is described in FIG. 3.

Performing operations using correlithm objects 104 in a correlithmobject domain allows the user device 100 to identify relationshipsbetween data samples that cannot be identified using conventional dataprocessing systems. For example, in the correlithm object domain, theuser device 100 is able to identify not only data samples that exactlymatch an input data sample, but also other data samples that havesimilar characteristics or features as the input data samples.Conventional computers are unable to identify these types ofrelationships readily. Using correlithm objects 104 improves theoperation of the user device 100 by enabling the user device 100 toefficiently process data samples and identify relationships between datasamples without relying on signal processing techniques that require asignificant amount of processing resources. These benefits allow theuser device 100 to operate more efficiently than conventional computersby reducing the amount of processing power and resources that are neededto perform various operations.

FIG. 2 is a schematic view of an embodiment of a mapping betweencorrelithm objects 104 in different n-dimensional spaces 102. Whenimplementing a correlithm object processing system, the user device 100performs operations within the correlithm object domain using correlithmobjects 104 in different n-dimensional spaces 102. As an example, theuser device 100 may convert different types of data samples having realworld values into correlithm objects 104 in different n-dimensionalspaces 102. For instance, the user device 100 may convert data samplesof text into a first set of correlithm objects 104 in a firstn-dimensional space 102 and data samples of audio samples as a secondset of correlithm objects 104 in a second n-dimensional space 102.Conventional systems require data samples to be of the same type and/orformat in order to perform any kind of operation on the data samples. Insome instances, some types of data samples cannot be compared becausethere is no common format available. For example, conventional computersare unable to compare data samples of images and data samples of audiosamples because there is no common format. In contrast, the user device100 implementing a correlithm object processing system is able tocompare and perform operations using correlithm objects 104 in thecorrelithm object domain regardless of the type or format of theoriginal data samples.

In FIG. 2, a first set of correlithm objects 104A are defined within afirst n-dimensional space 102A and a second set of correlithm objects104B are defined within a second n-dimensional space 102B. Then-dimensional spaces may have the same number of dimensions or adifferent number of dimensions. For example, the first n-dimensionalspace 102A and the second n-dimensional space 102B may both be threedimensional spaces. As another example, the first n-dimensional space102A may be a three dimensional space and the second n-dimensional space102B may be a nine dimensional space. Correlithm objects 104 in thefirst n-dimensional space 102A and second n-dimensional space 102B aremapped to each other. In other words, a correlithm object 104A in thefirst n-dimensional space 102A may reference or be linked with aparticular correlithm object 104B in the second n-dimensional space102B. The correlithm objects 104 may also be linked with and referencedwith other correlithm objects 104 in other n-dimensional spaces 102.

In one embodiment, a data structure such as table 200 may be used to mapor link correlithm objects 104 in different n-dimensional spaces 102. Insome instances, table 200 is referred to as a node table. Table 200 isgenerally configured to identify a first plurality of correlithm objects104 in a first n-dimensional space 102 and a second plurality ofcorrelithm objects 104 in a second n-dimensional space 102. Eachcorrelithm object 104 in the first n-dimensional space 102 is linkedwith a correlithm object 104 is the second n-dimensional space 102. Forexample, table 200 may be configured with a first column 202 that listscorrelithm objects 104A as source correlithm objects and a second column204 that lists corresponding correlithm objects 104B as targetcorrelithm objects. In other examples, table 200 may be configured inany other suitable manner or may be implemented using any other suitabledata structure. In some embodiments, one or more mapping functions maybe used to convert between a correlithm object 104 in a firstn-dimensional space and a correlithm object 104 is a secondn-dimensional space.

FIG. 3 is a schematic view of an embodiment of a correlithm objectprocessing system 300 that is implemented by a user device 100 toperform operations using correlithm objects 104. The system 300generally comprises a sensor 302, a node 304, and an actor 306. Thesystem 300 may be configured with any suitable number and/orconfiguration of sensors 302, nodes 304, and actors 306. An example ofthe system 300 in operation is described in FIG. 4. In one embodiment, asensor 302, a node 304, and an actor 306 may all be implemented on thesame device (e.g. user device 100). In other embodiments, a sensor 302,a node 304, and an actor 306 may each be implemented on differentdevices in signal communication with each other for example over anetwork. In other embodiments, different devices may be configured toimplement any combination of sensors 302, nodes 304, and actors 306.

Sensors 302 serve as interfaces that allow a user device 100 to convertreal world data samples into correlithm objects 104 that can be used inthe correlithm object domain. Sensors 302 enable the user device 100 tocompare and perform operations using correlithm objects 104 regardlessof the data type or format of the original data sample. Sensors 302 areconfigured to receive a real world value 320 representing a data sampleas an input, to determine a correlithm object 104 based on the realworld value 320, and to output the correlithm object 104. For example,the sensor 302 may receive an image 301 of a person and output acorrelithm object 322 to the node 304 or actor 306. In one embodiment,sensors 302 are configured to use sensor tables 308 that link aplurality of real world values with a plurality of correlithm objects104 in an n-dimensional space 102. Real world values are any type ofsignal, value, or representation of data samples. Examples of real worldvalues include, but are not limited to, images, pixel values, text,audio signals, electrical signals, and biometric signals. As an example,a sensor table 308 may be configured with a first column 312 that listsreal world value entries corresponding with different images and asecond column 314 that lists corresponding correlithm objects 104 asinput correlithm objects. In other examples, sensor tables 308 may beconfigured in any other suitable manner or may be implemented using anyother suitable data structure. In some embodiments, one or more mappingfunctions may be used to translate between a real world value 320 and acorrelithm object 104 in an n-dimensional space. Additional informationfor implementing or emulating a sensor 302 in hardware is described inFIG. 5.

Nodes 304 are configured to receive a correlithm object 104 (e.g. aninput correlithm object 104), to determine another correlithm object 104based on the received correlithm object 104, and to output theidentified correlithm object 104 (e.g. an output correlithm object 104).In one embodiment, nodes 304 are configured to use node tables 200 thatlink a plurality of correlithm objects 104 from a first n-dimensionalspace 102 with a plurality of correlithm objects 104 in a secondn-dimensional space 102. A node table 200 may be configured similar tothe table 200 described in FIG. 2. Additional information forimplementing or emulating a node 304 in hardware is described in FIG. 5.

Actors 306 serve as interfaces that allow a user device 100 to convertcorrelithm objects 104 in the correlithm object domain back to realworld values or data samples. Actors 306 enable the user device 100 toconvert from correlithm objects 104 into any suitable type of real worldvalue. Actors 306 are configured to receive a correlithm object 104(e.g. an output correlithm object 104), to determine a real world outputvalue 326 based on the received correlithm object 104, and to output thereal world output value 326. The real world output value 326 may be adifferent data type or representation of the original data sample. As anexample, the real world input value 320 may be an image 301 of a personand the resulting real world output value 326 may be text 327 and/or anaudio signal identifying the person. In one embodiment, actors 306 areconfigured to use actor tables 310 that link a plurality of correlithmobjects 104 in an n-dimensional space 102 with a plurality of real worldvalues. As an example, an actor table 310 may be configured with a firstcolumn 316 that lists correlithm objects 104 as output correlithmobjects and a second column 318 that lists real world values. In otherexamples, actor tables 310 may be configured in any other suitablemanner or may be implemented using any other suitable data structure. Insome embodiments, one or more mapping functions may be employed totranslate between a correlithm object 104 in an n-dimensional space anda real world output value 326. Additional information for implementingor emulating an actor 306 in hardware is described in FIG. 5.

A correlithm object processing system 300 uses a combination of a sensortable 308, a node table 200, and/or an actor table 310 to provide aspecific set of rules that improve computer-related technologies byenabling devices to compare and to determine the degree of similaritybetween different data samples regardless of the data type and/or formatof the data sample they represent. The ability to directly compare datasamples having different data types and/or formatting is a newfunctionality that cannot be performed using conventional computingsystems and data structures. Conventional systems require data samplesto be of the same type and/or format in order to perform any kind ofoperation on the data samples. In some instances, some types of datasamples are incompatible with each other and cannot be compared becausethere is no common format available. For example, conventional computersare unable to compare data samples of images with data samples of audiosamples because there is no common format available. In contrast, adevice implementing a correlithm object processing system uses acombination of a sensor table 308, a node table 200, and/or an actortable 310 to compare and perform operations using correlithm objects 104in the correlithm object domain regardless of the type or format of theoriginal data samples. The correlithm object processing system 300 usesa combination of a sensor table 308, a node table 200, and/or an actortable 310 as a specific set of rules that provides a particular solutionto dealing with different types of data samples and allows devices toperform operations on different types of data samples using correlithmobjects 104 in the correlithm object domain. In some instances,comparing data samples as correlithm objects 104 is computationally moreefficient and faster than comparing data samples in their originalformat. Thus, using correlithm objects 104 to represent data samplesprovides increased flexibility and improved performance compared tousing other conventional data structures. The specific set of rules usedby the correlithm object processing system 300 go beyond simply usingroutine and conventional activities in order to achieve this newfunctionality and performance improvements.

In addition, correlithm object processing system 300 uses a combinationof a sensor table 308, a node table 200, and/or an actor table 310 toprovide a particular manner for transforming data samples betweenordinal number representations and correlithm objects 104 in acorrelithm object domain. For example, the correlithm object processingsystem 300 may be configured to transform a representation of a datasample into a correlithm object 104, to perform various operations usingthe correlithm object 104 in the correlithm object domain, and totransform a resulting correlithm object 104 into another representationof a data sample. Transforming data samples between ordinal numberrepresentations and correlithm objects 104 involves fundamentallychanging the data type of data samples between an ordinal number systemand a categorical number system to achieve the previously describedbenefits of the correlithm object processing system 300.

FIG. 4 is a protocol diagram of an embodiment of a correlithm objectprocess flow 400. A user device 100 implements process flow 400 toemulate a correlithm object processing system 300 to perform operationsusing correlithm object 104 such as facial recognition. The user device100 implements process flow 400 to compare different data samples (e.g.images, voice signals, or text) to each other and to identify otherobjects based on the comparison. Process flow 400 provides instructionsthat allows user devices 100 to achieve the improved technical benefitsof a correlithm object processing system 300.

Conventional systems are configured to use ordinal numbers foridentifying different data samples. Ordinal based number systems onlyprovide information about the sequence order of numbers based on theirnumeric values, and do not provide any information about any other typesof relationships for the data samples being represented by the numericvalues such as similarity. In contrast, a user device 100 can implementor emulate the correlithm object processing system 300 which provides anunconventional solution that uses categorical numbers and correlithmobjects 104 to represent data samples. For example, the system 300 maybe configured to use binary integers as categorical numbers to generatecorrelithm objects 104 which enables the user device 100 to performoperations directly based on similarities between different datasamples. Categorical numbers provide information about how similardifferent data sample are from each other. Correlithm objects 104generated using categorical numbers can be used directly by the system300 for determining how similar different data samples are from eachother without relying on exact matches, having a common data type orformat, or conventional signal processing techniques.

A non-limiting example is provided to illustrate how the user device 100implements process flow 400 to emulate a correlithm object processingsystem 300 to perform facial recognition on an image to determine theidentity of the person in the image. In other examples, the user device100 may implement process flow 400 to emulate a correlithm objectprocessing system 300 to perform voice recognition, text recognition, orany other operation that compares different objects.

At step 402, a sensor 302 receives an input signal representing a datasample. For example, the sensor 302 receives an image of person's faceas a real world input value 320. The input signal may be in any suitabledata type or format. In one embodiment, the sensor 302 may obtain theinput signal in real-time from a peripheral device (e.g. a camera). Inanother embodiment, the sensor 302 may obtain the input signal from amemory or database.

At step 404, the sensor 302 identifies a real world value entry in asensor table 308 based on the input signal. In one embodiment, thesystem 300 identifies a real world value entry in the sensor table 308that matches the input signal. For example, the real world value entriesmay comprise previously stored images. The sensor 302 may compare thereceived image to the previously stored images to identify a real worldvalue entry that matches the received image. In one embodiment, when thesensor 302 does not find an exact match, the sensor 302 finds a realworld value entry that closest matches the received image.

At step 406, the sensor 302 identifies and fetches an input correlithmobject 104 in the sensor table 308 linked with the real world valueentry. At step 408, the sensor 302 sends the identified input correlithmobject 104 to the node 304. In one embodiment, the identified inputcorrelithm object 104 is represented in the sensor table 308 using acategorical binary integer string. The sensor 302 sends the binarystring representing to the identified input correlithm object 104 to thenode 304.

At step 410, the node 304 receives the input correlithm object 104 anddetermines distances 106 between the input correlithm object 104 andeach source correlithm object 104 in a node table 200. In oneembodiment, the distance 106 between two correlithm objects 104 can bedetermined based on the differences between the bits of the twocorrelithm objects 104. In other words, the distance 106 between twocorrelithm objects can be determined based on how many individual bitsdiffer between a pair of correlithm objects 104. The distance 106between two correlithm objects 104 can be computed using Hammingdistance or any other suitable technique. In another embodiment, thedistance 106 between two correlithm objects 104 can be determined usinga Minkowski distance such as the Euclidean or “straight-line” distancebetween the correlithm objects 104. For example, the distance 106between a pair of correlithm objects 104 may be determined bycalculating the square root of the sum of squares of the coordinatedifference in each dimension.

At step 412, the node 304 identifies a source correlithm object 104 fromthe node table 200 with the shortest distance 106. A source correlithmobject 104 with the shortest distance from the input correlithm object104 is a correlithm object 104 either matches or most closely matchesthe received input correlithm object 104.

At step 414, the node 304 identifies and fetches a target correlithmobject 104 in the node table 200 linked with the source correlithmobject 104. At step 416, the node 304 outputs the identified targetcorrelithm object 104 to the actor 306. In this example, the identifiedtarget correlithm object 104 is represented in the node table 200 usinga categorical binary integer string. The node 304 sends the binarystring representing to the identified target correlithm object 104 tothe actor 306.

At step 418, the actor 306 receives the target correlithm object 104 anddetermines distances between the target correlithm object 104 and eachoutput correlithm object 104 in an actor table 310. The actor 306 maycompute the distances between the target correlithm object 104 and eachoutput correlithm object 104 in an actor table 310 using a processsimilar to the process described in step 410.

At step 420, the actor 306 identifies an output correlithm object 104from the actor table 310 with the shortest distance 106. An outputcorrelithm object 104 with the shortest distance from the targetcorrelithm object 104 is a correlithm object 104 either matches or mostclosely matches the received target correlithm object 104.

At step 422, the actor 306 identifies and fetches a real world outputvalue in the actor table 310 linked with the output correlithm object104. The real world output value may be any suitable type of data samplethat corresponds with the original input signal. For example, the realworld output value may be text that indicates the name of the person inthe image or some other identifier associated with the person in theimage. As another example, the real world output value may be an audiosignal or sample of the name of the person in the image. In otherexamples, the real world output value may be any other suitable realworld signal or value that corresponds with the original input signal.The real world output value may be in any suitable data type or format.

At step 424, the actor 306 outputs the identified real world outputvalue. In one embodiment, the actor 306 may output the real world outputvalue in real-time to a peripheral device (e.g. a display or a speaker).In one embodiment, the actor 306 may output the real world output valueto a memory or database. In one embodiment, the real world output valueis sent to another sensor 302. For example, the real world output valuemay be sent to another sensor 302 as an input for another process.

FIG. 5 is a schematic diagram of an embodiment of a computerarchitecture 500 for emulating a correlithm object processing system 300in a user device 100. The computer architecture 500 comprises aprocessor 502, a memory 504, a network interface 506, and aninput-output (I/O) interface 508. The computer architecture 500 may beconfigured as shown or in any other suitable configuration.

The processor 502 comprises one or more processors operably coupled tothe memory 504. The processor 502 is any electronic circuitry including,but not limited to, state machines, one or more central processing unit(CPU) chips, logic units, cores (e.g. a multi-core processor),field-programmable gate array (FPGAs), application specific integratedcircuits (ASICs), graphics processing units (GPUs), or digital signalprocessors (DSPs). The processor 502 may be a programmable logic device,a microcontroller, a microprocessor, or any suitable combination of thepreceding. The processor 502 is communicatively coupled to and in signalcommunication with the memory 204. The one or more processors areconfigured to process data and may be implemented in hardware orsoftware. For example, the processor 502 may be 8-bit, 16-bit, 32-bit,64-bit or of any other suitable architecture. The processor 502 mayinclude an arithmetic logic unit (ALU) for performing arithmetic andlogic operations, processor registers that supply operands to the ALUand store the results of ALU operations, and a control unit that fetchesinstructions from memory and executes them by directing the coordinatedoperations of the ALU, registers and other components.

The one or more processors are configured to implement variousinstructions. For example, the one or more processors are configured toexecute instructions to implement sensor engines 510, node engines 512,and actor engines 514. In an embodiment, the sensor engines 510, thenode engines 512, and the actor engines 514 are implemented using logicunits, FPGAs, ASICs, DSPs, or any other suitable hardware. The sensorengines 510, the node engines 512, and the actor engines 514 are eachconfigured to implement a specific set of rules or process that providesan improved technological result.

In one embodiment, the sensor engine 510 is configured to receive a realworld value 320 as an input, to determine a correlithm object 104 basedon the real world value 320, and to output the correlithm object 104.Examples of the sensor engine 510 in operation are described in FIG. 4.

In one embodiment, the node engine 512 is configured to receive acorrelithm object 104 (e.g. an input correlithm object 104), todetermine another correlithm object 104 based on the received correlithmobject 104, and to output the identified correlithm object 104 (e.g. anoutput correlithm object 104). The node engine 512 is also configured tocompute distances between pairs of correlithm objects 104. Examples ofthe node engine 512 in operation are described in FIGS. 4 and 6-13.

In one embodiment, the actor engine 514 is configured to receive acorrelithm object 104 (e.g. an output correlithm object 104), todetermine a real world output value 326 based on the received correlithmobject 104, and to output the real world output value 326. Examples ofthe actor engine 514 in operation are described in FIGS. 4 and 6-13.

The memory 504 comprises one or more non-transitory disks, tape drives,or solid-state drives, and may be used as an over-flow data storagedevice, to store programs when such programs are selected for execution,and to store instructions and data that are read during programexecution. The memory 504 may be volatile or non-volatile and maycomprise read-only memory (ROM), random-access memory (RAM), ternarycontent-addressable memory (TCAM), dynamic random-access memory (DRAM),and static random-access memory (SRAM). The memory 504 is operable tostore sensor instructions 516, node instructions 518, actor instructions520, sensor tables 308, node tables 200, actor tables 310, and/or anyother data or instructions. The sensor instructions 516, the nodeinstructions 518, and the actor instructions 520 comprise any suitableset of instructions, logic, rules, or code operable to execute thesensor engine 510, node engine 512, and the actor engine 514,respectively.

The sensor tables 308, the node tables 200, and the actor tables 310 maybe configured similar to the sensor tables 308, the node tables 200, andthe actor tables 310 described in FIG. 3, respectively.

The network interface 506 is configured to enable wired and/or wirelesscommunications. The network interface 506 is configured to communicatedata with any other device or system. For example, the network interface506 may be configured for communication with a modem, a switch, arouter, a bridge, a server, or a client. The processor 502 is configuredto send and receive data using the network interface 506.

The I/O interface 508 may comprise ports, transmitters, receivers,transceivers, or any other devices for transmitting and/or receivingdata with peripheral devices as would be appreciated by one of ordinaryskill in the art upon viewing this disclosure. For example, the I/Ointerface 508 may be configured to communicate data between theprocessor 502 and peripheral hardware such as a graphical userinterface, a display, a mouse, a keyboard, a key pad, and a touch sensor(e.g. a touch screen).

FIGS. 6A-B illustrate one embodiment of a correlithm object processingsystem 610 that includes a distribution node 602, calculation nodes 604a and 604 b through 604 n, and a resolution node 606, allcommunicatively coupled to each other. FIGS. 8A-B illustrate oneembodiment of a correlithm object processing system 810 that includes adistribution node 802, calculation nodes 804 a and 804 b through 804 n,and a resolution node 806, all communicatively coupled to each other.FIGS. 9A-B illustrate one embodiment of a correlithm object processingsystem 910 that includes a distribution node 902, calculation nodes 904a and 904 b through 904 n, and a resolution node 906, allcommunicatively coupled to each other. FIGS. 12A-B illustrate oneembodiment of a correlithm object processing system 1210 that includes adistribution node 1202, calculation nodes 1204 a and 1204 b through 1204n, and a resolution node 1206, all communicatively coupled to eachother. Systems 610, 810, 910, and 1210 can each be implemented in acomputer architecture 500 (as illustrated in FIG. 5) associated with oneor more user devices 100 (as illustrated in FIG. 1) to implement avariety of computing environments. Accordingly, nodes 602, 604 a-n, and606, nodes 802, 804 a-n, and 806, nodes 902, 904 a-n, and 906, and/ornodes 1202, 1212 a-n, 1214 a-n, and 1206 may be implemented using anysuitable number and combination of node engines 512, processors 502, andmemories 504. In some examples, nodes 602, 604 a-n, and 606, nodes 802,804 a-n, and 806, nodes 902, 904 a-n, and 906, and/or nodes 1202, 1212a-n, 1214 a-n, and 1206 may reside in a primarily centralized orstand-alone environment. Alternatively, or in addition, systems 610,810, 910, and/or 1210 can be implemented such that nodes 602, 604 a-n,and 606, nodes 802, 804 a-n, and 806, nodes 902, 904 a-n, and 906,and/or nodes 1202, 1212 a-n, 1214 a-n, and 1206 reside in a primarilydistributed or cloud computing environment. Computer architecture 500used to implement systems 610, 810, 910, and/or 1210 can leverage anynumber and combination of computing paradigms, such as parallelcomputing, concurrent computing, distributed computing, and the like.

In parallel computing, a computational task is typically broken downinto several, often many, similar subtasks that can be processedindependently and whose results are combined afterwards, uponcompletion. In concurrent computing, the various processes may notaddress related tasks; when they do, as is typical in distributedcomputing, the separate tasks may have a varied nature and often involvesome inter-process communication during execution. In distributedcomputing, a distributed computer (also known as a distributed memorymultiprocessor) is a distributed memory computer system in which theprocessing elements are connected by a network. Each of these computingparadigms are highly scalable.

The computing environments in which systems 610, 810, 910, and/or 1210are implemented to support parallel processing, whereby their respectivenodes 602, 604 a-n, and 606, nodes 802, 804 a-n, and 806, nodes 902, 904a-n, and 906, and/or nodes 1202, 1212 a-n, 1214 a-n, and 1206 canexecute simultaneously on different processors or servers associatedwith the same or different computers. In this way, systems 610, 810,910, 1210 can leverage multi-core, multi-threaded and multi-processorcomputers having multiple processing elements within a single machine;cluster computing (composed of multiple standalone machines connected bya network); massively parallel processors (a single computer with manynetworked processors); symmetric multiprocessors (a computer system withmultiple identical processors that share memory and connect via a bus);and grid computing (computers communicating over the Internet to work ona given problem), among others understood by persons of ordinary skillin the art of computing.

Referring now to FIG. 6A, distribution node 602 stores a correlithmobject mapping table 200 configured with a first column 202 thatincludes source correlithm objects 104A and a second column 204 thatincludes corresponding target correlithm objects 104B. Although table200 is described with respect to columns 202 and 204, one of ordinaryskill in the art will appreciate that any suitable organization of dataor data structure that can map source correlithm objects 104A to targetcorrelithm objects 104B can be used in system 610. The source correlithmobjects 104A and target correlithm objects 104B each reside in the sameor different n-dimensional spaces 102. In one embodiment, sourcecorrelithm objects 104A and target correlithm objects 104B are eachn-bit digital words comprising binary values. For example, they maycomprise 64-bit, 128-bit, or 256-bit digital words comprising a binarystring of values.

As described above with respect to FIG. 3, a node 304 is generallyconfigured to receive an input correlithm object 104, identify thesource correlithm object 104A that most closely matches the inputcorrelithm object 104, and output the target correlithm object 104Bcorresponding to the identified source correlithm object 104A. Findingthe source correlithm object 104A that most closely matches the inputcorrelithm object 104 may involve computing the n-dimensional distance(e.g., Hamming distance, Minkowski distance, or other suitable distance)between the input correlithm object 104 and each of the sourcecorrelithm objects 104A, and identifying the source correlithm object104A that results in the smallest n-dimensional distance calculation.Performing this distance calculation serially for a relatively largenumber of source correlithm objects 104A in a relatively largecorrelithm object mapping table 200 can be time and resource consuming,which can create bottlenecks in a correlithm object processing system.The present disclosure provides a technical solution whereby then-dimensional distance calculation is performed using parallelprocessing techniques implemented by calculation nodes 604 a-n, andresolution node 606, as described in greater detail below.

Distribution node 602 divides correlithm object mapping table 200 intoany suitable number of portions, such as first portion 200 a of thecorrelithm object mapping table 200, a second portion 200 b of thecorrelithm object mapping table 200, and an n-th portion 200 n of thecorrelithm object mapping table 200. The first portion 200 a includes afirst subset of the source correlithm objects 104A and theircorresponding target correlithm objects 104B from the entire correlithmobject mapping table 200. For example, as illustrated in FIG. 6A, thefirst portion 200 a of the correlithm object mapping table 200 includessource correlithm objects 1-3 and the corresponding target correlithmobjects 1-3. The second portion 200 b includes a second subset of thesource correlithm objects 104A and their corresponding target correlithmobjects 104B from the entire correlithm object mapping table 200. Forexample, as illustrated in FIG. 6A, the second portion 200 b includessource correlithm objects a-c and the corresponding target correlithmobjects a-c. The n-th portion of the 200 n includes an n-th subset ofthe source correlithm objects 104A and their corresponding targetcorrelithm objects 104B from the entire correlithm object mapping table200. For example, as illustrated in FIG. 6A, the n-th portion 200 nincludes source correlithm objects x-z and the corresponding targetcorrelithm objects x-z. In some embodiments, the correlithm objects 104included in the first, second, and n-th portions of the mapping table200 are mutually exclusive of each other, or non-overlapping. In otherembodiments, the correlithm objects 104 included in the first, second,and/or n-th portions of the mapping table 200 are overlapping or atleast partially overlapping in order to achieve redundancy in thedistance calculations between the input correlithm object 104 and thesource correlithm objects 104A as detailed below.

The first, second, and n-th portions of mapping table 200 are eachillustrated as having three source and target correlithm objects forillustrative purposes only. They may have any suitable number ofcorrelithm objects 104 that may be the same or different from eachother. In one embodiment, the number of correlithm objects 104 that areincluded in the first, second, and n-th portions of the mapping table200 is determined based on the computing power of the correspondingcalculation nodes 604 a, 604 b, and 604 n, and/or the speed with whichthose nodes 604 a-n can perform their processes. For example, the morecomputing power and/or speed that a particular node 604 can perform itsprocesses, the larger number of correlithm objects 104 can betransmitted to that node 604 in the relevant portion of the mappingtable 200.

Distribution node 602 transmits the first portion 200 a of thecorrelithm object mapping table 200 to the first calculation node 604 a,the second portion 200 b to the second calculation node 604 b, and then-th portion 200 n to the n-th calculation node 604 n. Aftertransmission, the first calculation node 604 a stores, or otherwise hasaccess to, the first portion 200 a; the second calculation node 604 bstores, or otherwise has access to, the second portion 200 b; and then-th calculation node 604 n stores, or otherwise has access to, the n-thportion 200 n. Although system 600 is primarily described as havingdistribution node 602 divide the mapping table 200 into portions 200 a-nand transmit those portions 200 a-n to corresponding calculation nodes604 a-n, it should be understood that the mapping table 200 caninitially be created in multiple portions 200 a-n and stored, orotherwise accessed by, calculation nodes 604 a-n without the need todivide the mapping table 200 and transmit the portions 200 a-n by thedistribution node 602. Either approach achieves the technical advantagesof parallel processing in computing the n-dimensional distances betweenthe input correlithm object 104 and the source correlithm objects 104Ain the portions 200 a-n of the mapping table 200.

In operation, each of the calculation nodes 604 a-n receives an inputcorrelithm object 104. Although FIG. 6A illustrates nodes 604 a-nreceiving the input correlithm object 104 from distribution node 602,nodes 604 a-n may receive input correlithm object 104 from any suitablesources including, but not limited to, distribution node 602. The inputcorrelithm object 104 is an n-bit digital word comprising binary values.Each of the calculation nodes 604 a-n determines the n-dimensionaldistance (e.g., Hamming distance, Minkowski distance, or other suitabledistance calculation) between the input correlithm object 104 and thesource correlithm objects 104A stored in the relevant portions 200 a-nof the mapping table 200 for that node 604 a-n. For example, calculationnode 604 a determines the n-dimensional distance between the inputcorrelithm object 104 and the source correlithm objects 1-3 stored inthe first portion 200 a of the mapping table 200. With respect tocalculating a Hamming distance, as described above with respect to atleast FIG. 1, the determined n-dimensional distances are based ondifferences between the binary values representing the input correlithmobject 104 and the binary values representing each source correlithmobject 1-3 of the first portion 200 a of the mapping table 200.

Calculation node 604 a identifies the source correlithm object 1-3 withthe closest n-dimensional distance (e.g., smallest Hamming distance) tothe input correlithm object 104. Calculation node 604 a furtheridentifies the target correlithm object 104B that corresponds to theidentified source correlithm object. In this example, assume that thecorrelithm objects described herein are 256-bit digital values. Furtherassume that the smallest Hamming distance is calculated to be 10 and isassociated with source correlithm object 2. Calculation node 604 atransmits the smallest calculated Hamming distance (i.e., 10) and thecorresponding target correlithm object 2 to resolution node 606 forfurther processing, as detailed below.

Continuing with the example, calculation node 604 b determines then-dimensional distance between the input correlithm object 104 and thesource correlithm objects a-c stored in the second portion 200 b of themapping table 200. With respect to calculating a Hamming distance, asdescribed above with respect to at least FIG. 1, the determinedn-dimensional distances are based on differences between the binaryvalues representing the input correlithm object 104 and the binaryvalues representing each source correlithm object a-c of the secondportion 200 b of the mapping table 200.

Calculation node 604 b identifies the source correlithm object a-c withthe closest n-dimensional distance (e.g., smallest Hamming distance) tothe input correlithm object 104. Calculation node 604 b furtheridentifies the target correlithm object 104B that corresponds to theidentified source correlithm object. In this example using 256-bitdigital values for the correlithm objects, assume that the smallestHamming distance is calculated to be 3 and is associated with sourcecorrelithm object c. Calculation node 604 b transmits the smallestcalculated Hamming distance (i.e., 3) and the corresponding targetcorrelithm object c to resolution node 606 for further processing, asdetailed below.

Continuing with the example, calculation node 604 n determines then-dimensional distance between the input correlithm object 104 and thesource correlithm objects x-z stored in the n-th portion 200 n of themapping table 200. With respect to calculating a Hamming distance, asdescribed above with respect to at least FIG. 1, the determinedn-dimensional distances are based on differences between the binaryvalues representing the input correlithm object 104 and the binaryvalues representing each source correlithm object x-z of the n-thportion 200 n of the mapping table 200.

Calculation node 604 n identifies the source correlithm object x-z withthe closest n-dimensional distance (e.g., smallest Hamming distance) tothe input correlithm object 104. Calculation node 604 n furtheridentifies the target correlithm object 104B that corresponds to theidentified source correlithm object. In this example using 256-bitdigital values for the correlithm objects, assume that the smallestHamming distance is calculated to be 45 and is associated with sourcecorrelithm object x. Calculation node 604 n transmits the smallestcalculated Hamming distance (i.e., 45) and the corresponding targetcorrelithm object x to resolution node 606 for further processing, asdetailed below.

Resolution node 606 determines the appropriate target correlithm object104B to transmit as the output correlithm object 104 based on acomparison of the smallest calculated n-dimensional distancescommunicated by each of the calculation nodes 604 a-n. In particular,resolution node 606 compares the determined n-dimensional distance(i.e., Hamming distance of 10) associated with the identified sourcecorrelithm object 2 from the first calculation node 604 a, with thedetermined n-dimensional distance (i.e., Hamming distance of 3)associated with the identified source correlithm object c from thesecond calculation node 604 b, and the determined n-dimensional distance(i.e., Hamming distance of 45) associated with the identified sourcecorrelithm object x from the n-th calculation node 604 n. Resolutionnode 606 identifies the smallest determined n-dimensional distance amongall of the distances transmitted to it based on this comparison.Accordingly, in this example, resolution node 606 determines that thesmallest determined n-dimensional distance transmitted to it is aHamming distance of 3 from second calculation node 604 b. Resolutionnode 606 identifies the target correlithm object 104B associated withthe smallest determined n-dimensional distance, and outputs theidentified target correlithm object 104B as output correlithm object104. In this example, resolution node 606 identifies the targetcorrelithm object c as being associated with the smallest determinedn-dimensional distance of 3 and communicates it as output correlithmobject 104.

In the described example, this means that of all the source correlithmobjects 104A stored in the different portions of the mapping table 200a-n, the input correlithm object 104 most closely matched the sourcecorrelithm object c in the second portion 200 b of the mapping table200. By dividing the mapping table 200 into smaller portions 200 a-n,the process of determining the n-dimensional distances between the inputcorrelithm object 104 and the source correlithm objects 104 wasperformed in parallel by calculation nodes 604 a-n. Performing parallelprocessing, as described above, increases the speed and efficiency ofcalculating the n-dimensional distances between the input correlithmobject 104 and the source correlithm objects of the mapping table 200.This reduces bottlenecks in the network and in the overall correlithmobject processing system 600.

FIG. 6B illustrates an embodiment of correlithm object processing system610 that operates in conjunction with an actor table 310 rather than acorrelithm object mapping table 200. In this embodiment, actor table 310is configured with a first column 202 that includes target correlithmobjects 104B and a second column 204 that includes real world datavalues. The operation of system 610 in FIG. 6B is substantially similarto that of system 610 in FIG. 6A except that distribution node 602,calculation nodes 604 a-n, and resolution node 606 operate using targetcorrelithm objects 104B from actor table 310 in place of sourcecorrelithm objects 104A from mapping table 200, and they operate usingreal world data values from actor table 310 in place of targetcorrelithm objects 104B from mapping table 200.

FIG. 7 illustrates one embodiment of a flowchart 700 implementing aprocess performed by the components of correlithm object processingsystem 610. Upon starting the process, distribution node 602 storescorrelithm object mapping table 200 at step 702. Mapping table 200includes source correlithm objects 104A mapped to target correlithmobjects 104B. Distribution node 602 divides mapping table 200 intomultiple portions, such as first portion 200 a, second portion 200 b,and n-th portion 200 n, at step 704. Each portion of mapping table 200includes a subset of the source correlithm objects 104A andcorresponding target correlithm objects 104B. Execution proceeds to step706, where distribution node 602 transmits first portion 200 a ofmapping table 200 to first calculation node 604 a, second portion 200 bof mapping table 200 to second calculation node 604 b, and n-th portion200 n of mapping table 200 to n-th calculation node 604 n.

Execution proceeds in parallel according to processing path 708associated with first calculation node 604 a, path 710 associated withsecond calculation node 604 b, and path 712 associated with n-thcalculation node 604 n. Notwithstanding the ordering of discussion withrespect to the flowchart illustrated in FIG. 7, the processes describedin each of the paths 708, 710, and 712 are performed substantiallysimultaneously. Furthermore, as described above, although the flowchart700 is described as beginning with the operation of distribution node602 dividing and transmitting portions of a mapping table 200 tocalculation nodes 604 a-n, it should be understood that in at least oneembodiment, the process could start with portions of the mapping table200 already stored or otherwise accessible by the appropriatecalculation nodes 604 a-n.

Referring to processing path 708, execution proceeds to step 720 wherefirst calculation node 604 a stores first portion 200 a of mapping table200. First calculation node 604 a receives input correlithm object 104at step 722. Execution proceeds to step 724 where first calculation node604 a determines n-dimensional distances between the input correlithmobject 104 and each of the source correlithm objects 1-3 in the firstportion 200 a of the mapping table 200. The determined n-dimensionaldistances are based on differences between the binary valuesrepresenting the input correlithm object 104 and the binary valuesrepresenting each source correlithm object 1-3 of the first portion 200a of the mapping table 200. At step 726, first calculation node 604 aidentifies the smallest n-dimensional distance from among the determineddistances, and identifies the source correlithm object 1-3 from thefirst portion 200 a of the mapping table 200 that is the closest matchto the input correlithm object 104. Execution proceeds to step 728 wherefirst calculation node 604 a identifies the target correlithm object104B from the first portion 200 a of the mapping table 200 thatcorresponds to the source correlithm object 104A identified at step 726.At step 730, first calculation node 604 a transmits to resolution node606 the target correlithm object 104B identified at step 728, and thesmallest determined n-dimensional distance identified at step 726.

Referring to processing path 710, execution proceeds to step 732 wheresecond calculation node 604 b stores second portion 200 b of mappingtable 200. Second calculation node 604 b receives input correlithmobject 104 at step 734. Execution proceeds to step 736 where secondcalculation node 604 b determines n-dimensional distances between theinput correlithm object 104 and each of the source correlithm objectsa-c in the second portion 200 b of the mapping table 200. The determinedn-dimensional distances are based on differences between the binaryvalues representing the input correlithm object 104 and the binaryvalues representing each source correlithm object a-c of the secondportion 200 b of the mapping table 200. At step 738, second calculationnode 604 b identifies the smallest n-dimensional distance from among thedetermined distances, and identifies the source correlithm object 104Afrom the second portion 200 b of the mapping table 200 that is theclosest match to the input correlithm object 104. Execution proceeds tostep 740 where second calculation node 604 b identifies the targetcorrelithm object 104B from the second portion 200 b of the mappingtable 200 that corresponds to the source correlithm object 104Aidentified at step 738. At step 742, second calculation node 604 btransmits to resolution node 606 the target correlithm object 104Bidentified at step 740, and the smallest determined n-dimensionaldistance identified at step 738.

Referring to processing path 712, execution proceeds to step 744 wheren-th calculation node 604 n stores n-th portion 200 n of mapping table200. N-th calculation node 604 n receives input correlithm object 104 atstep 746. Execution proceeds to step 748 where n-th calculation node 604n determines n-dimensional distances between the input correlithm object104 and each of the source correlithm objects x-z in the n-th portion200 n of the mapping table 200. The determined n-dimensional distancesare based on differences between the binary values representing theinput correlithm object 104 and the binary values representing eachsource correlithm object x-z of the n-th portion 200 n of the mappingtable 200. At step 750, n-th calculation node 604 n identifies thesmallest n-dimensional distance from among the determined distances, andidentifies the source correlithm object 104A from the n-th portion 200 nof the mapping table 200 that is the closest match to the inputcorrelithm object 104. Execution proceeds to step 752 where n-thcalculation node 604 n identifies the target correlithm object 104B fromthe n-th portion 200 n of the mapping table 200 that corresponds to thesource correlithm object 104A identified at step 750. At step 754, n-thcalculation node 604 n transmits to resolution node 606 the targetcorrelithm object 104B identified at step 752 and the smallestdetermined n-dimensional distance identified at step 750.

Execution proceeds from paths 708, 710, and 712 to step 760 whereresolution node 606 compares the n-dimensional distances transmitted bythe first calculation node 604 a, the second calculation node 604 b, andthe n-th calculation node 604 n. At step 762, resolution node 606identifies the smallest n-dimensional distance based on the comparisonperformed at step 760. Execution proceeds to step 764, where resolutionnode 606 identifies the target correlithm object 104B associated withthe smallest n-dimensional distance determined at step 762. At step 766,resolution node 606 outputs the target correlithm object 104B identifiedat step 764. Execution terminates at step 768.

Referring now to FIG. 8A, distribution node 802 of system 810 stores acorrelithm object mapping table 200 configured with a first column 202that includes source correlithm objects 104A and a second column 204that includes corresponding target correlithm objects 104B. Althoughtable 200 is described with respect to columns 202 and 204, one ofordinary skill in the art will appreciate that any suitable organizationof data or data structure that can map source correlithm objects 104A totarget correlithm objects 104B can be used in system 810. The sourcecorrelithm objects 104A and target correlithm objects 104B each residein the same or different n-dimensional spaces 102. In one embodiment,source correlithm objects 104A and target correlithm objects 104B areeach n-bit digital words comprising binary values. For example, they maycomprise 64-bit, 128-bit, or 256-bit digital words comprising a binarystring of values.

As described above with respect to FIG. 3, a node 304 is generallyconfigured to receive an input correlithm object 104, identify thesource correlithm object 104A that most closely matches the inputcorrelithm object 104, and output the target correlithm object 104Bcorresponding to the identified source correlithm object 104A. Findingthe source correlithm object 104A that most closely matches the inputcorrelithm object 104 may involve computing the n-dimensional distance(e.g., Hamming distance, Minkowski distance, or other suitable distance)between the input correlithm object 104 and each of the sourcecorrelithm objects 104A, and identifying the source correlithm object104A that results in the smallest n-dimensional distance calculation.Performing this distance calculation serially for a relatively largenumber of source correlithm objects 104A in a relatively largecorrelithm object mapping table 200 can be time consuming, which cancreate bottlenecks in a correlithm object processing system. The presentdisclosure provides a technical solution whereby the n-dimensionaldistance calculation is performed using parallel processing techniquesimplemented by calculation nodes 804 a-n, and resolution node 806, asdescribed in greater detail below.

Distribution node 802 divides each source correlithm object 104A ofmapping table 200 into any suitable number of portions, such as firstportion 800 a, second portion 800 b, and n-th portion 800 n. Firstportion 800 a of a given source correlithm object 104A (e.g., sourceCO1) comprises a first subset of the n-bit digital word of binary valuesmaking up that source correlithm object 104A. For example, asillustrated in FIG. 8, if source CO1 is a 256-bit digital word of binaryvalues, then first portion 800 a of source CO1 may include the first 64bits of that 256-bit digital word. Second portion 800 b of that sourcecorrelithm object 104A (e.g., source CO1) comprises a second subset ofthe n-bit digital word of binary values making up that source correlithmobject 104A. For example, as illustrated in FIG. 8, if source CO1 is a256-bit digital word of binary values, then second portion 800 b ofsource CO1 may include the second 64 bits of that 256-bit digital word.N-th portion 800 n of that source correlithm object 104A (e.g., sourceCO1) comprises an n-th subset of the n-bit digital word of binary valuesmaking up that source correlithm object 104A. For example, asillustrated in FIG. 8, if source CO1 is a 256-bit digital word of binaryvalues, then n-th portion 800 n of source CO1 may include the last 128bits of that 256-bit digital word. Each of the other source correlithmobjects 104A stored in mapping table 200 (e.g., source CO2, source CO3,source COa, source COb, source COc, source COx, source COy, and sourceCOz) may be similarly divided into first portions 800 a, second portions800 b, and n-th portions 800 n.

The source correlithm objects 104A are shown as being divided into threeportions of 64-bit, 64-bit, and 128-bit sizes for illustrative purposesonly. The source correlithm objects 104A may be divided into anysuitable number of portions 800, each portion 800 having any suitablenumber of bits of the digital word. For example, a 256-bit sourcecorrelithm object 104A can be divided into four portions, each having 64bits. In another non-limiting example, a 256-bit source correlithmobject 104A can be divided into four portions having 64 bits, 32 bits,128 bits, and 32 bits, respectively. In still another non-limitingexample, a 256-bit source correlithm object 104A can be divided into tenportions, with nine of those portions having 25 bits and the tenthportion having 31 bits. The present disclosure contemplates any numberof other combinations for the number of portions 800 and the size ofeach portion 800. In one embodiment, the number and sizes of portions800 that are divided from the source correlithm object 104A in mappingtable 200 are determined based on the computing power of thecorresponding calculation nodes 804 a, 804 b, and 804 n, and/or thespeed with which those nodes 804 a-n can perform their processes. Forexample, the more computing power and/or speed that a particular node804 can perform its processes, the larger number of bits that can beincluded in a portion 800 that is transmitted to that node 804 forcalculation.

Distribution node 802 transmits the first portion 800 a of each sourcecorrelithm object 104A in mapping table 200 to the first calculationnode 804 a, the second portion 800 b of each source correlithm object104A in mapping table 200 to the second calculation node 804 b, and then-th portion 200 n of each source correlithm object 104A in mappingtable 200 to the n-th calculation node 804 n. After transmission, thefirst calculation node 804 a stores, or otherwise has access to, thefirst portions 800 a; the second calculation node 804 b stores, orotherwise has access to, the second portions 800 a; and the n-thcalculation node 804 n stores, or otherwise has access to, the n-thportions 800 n. Although system 800 is primarily described as havingdistribution node 802 divide the source correlithm objects 104A inmapping table 200 into portions 800 a-n and transmit those portions 200a-n to corresponding calculation nodes 804 a-n, it should be understoodthat the source correlithm objects 104A can initially be created alreadydivided and stored, or otherwise accessible by, calculation nodes 804a-n without the need for distribution node 802 to divide and transmitthem to the calculation nodes 804 a-n. Either approach achieves thetechnical advantages of parallel processing, as detailed below.

In operation, each of the calculation nodes 804 a-n receives an inputcorrelithm object 104 (or at least a portion thereof). In the exampledescribed below, the input correlithm object 104 is a 256-bit digitalword of binary values. Although FIG. 8 illustrates nodes 804 a-nreceiving the input correlithm object 104 from distribution node 802,nodes 804 a-n may receive input correlithm object 104 from any suitablesources including, but not limited to, distribution node 802. The inputcorrelithm object 104 is an n-bit digital word comprising binary values.

Each of the calculation nodes 804 a-n determines the n-dimensionaldistance (e.g., Hamming distance, Minkowski distance, or other suitabledistance calculation) between the relevant bits of the input correlithmobject 104 and the relevant portions 800 a-n of each source correlithmobject 104A. For example, calculation node 804 a determines then-dimensional distance between the first 64 bits of input correlithmobject 104 and the first portion 800 a of each source correlithm object104, which in this example is also 64 bits. Thus, in the exampleillustrated in FIG. 8, calculation node 804 a calculates ninen-dimensional distances, one each for the first portions 800 a of sourcecorrelithm objects 1-3, source correlithm objects a-c, and sourcecorrelithm objects x-z. Similarly, calculation node 804 a determines then-dimensional distance between the next 64 bits of input correlithmobject 104 and the second portion 800 a of each source correlithm object104, which in this example is also 64 bits. Thus, in the exampleillustrated in FIG. 8, calculation node 804 b calculates ninen-dimensional distances, one each for the second portions 800 b ofsource correlithm objects 1-3, source correlithm objects a-c, and sourcecorrelithm objects x-z. Finally, calculation node 804 n determines then-dimensional distance between the last 128 bits of input correlithmobject 104 and the n-th portion 800 n of each source correlithm object104, which in this example is also 128 bits. Thus, in the exampleillustrated in FIG. 8, calculation node 804 n calculates ninen-dimensional distances, one each for the first portions 800 n of sourcecorrelithm objects 1-3, source correlithm objects a-c, and sourcecorrelithm objects x-z. With respect to calculating a Hamming distance,as described above with respect to at least FIG. 1, the determinedn-dimensional distances are based on differences between the binaryvalues representing the input correlithm object 104 and the binaryvalues representing each of the portions 800 a-n of the sourcecorrelithm objects. Each calculation node 804 a-n transmits thedetermined n-dimensional distance values to the resolution node 806 forfurther processing, as detailed below.

Resolution node 806 receives the n-dimensional distance calculationsfrom each of the calculation nodes 804 a-n and adds them together foreach source correlithm object 104A. For example, resolution node 806adds together the n-dimensional distance determined for the firstportion 800 a of source correlithm object 1 with the n-dimensionaldistance determined for the second portion 800 a of source correlithmobject 1 and the n-dimensional distance determined for the n-th portion800 n of source correlithm object 1. Likewise, resolution node 806 addstogether the n-dimensional distances together for the first portion 800a, second portion 800 b, and n-th portion 800 n of source correlithmobject 2. Resolution node 806 further adds together the n-dimensionaldistances of the portions 800 a, 800 b, and 800 n for each of the othersource correlithm objects (e.g., source correlithm object 3, sourcecorrelithm objects a-c, and source correlithm objects x-z). Thus, inthis particular example, resolution node 806 will have calculated nineseparate n-dimensional distances based on these calculations, one eachfor the source correlithm objects 104A in mapping table 200. In aparticular embodiment, these n-dimensional distances may be representedas Hamming distances and added together as such. In other embodiments,they may be Minkowski distances, or any other suitable form ofmeasurement for n-dimensional distances.

Resolution node 806 then compares the determined n-dimensional distancesto identify the source correlithm object 104A with the smallestn-dimensional distance to the input correlithm object 104. For example,if the n-dimensional distances are measured using Hamming distances,then resolution node 806 will compare the aggregate Hamming distancescalculated for each of the nine source correlithm objects 1-3, a-c, andx-z, to determine which source correlithm object 104A has the smallestHamming distance. Resolution node 806 then identifies the targetcorrelithm object 104B that corresponds to the identified sourcecorrelithm object 104A with the smallest n-dimensional distance to theinput correlithm object 104, and communicates that target correlithmobject 104B as output correlithm object 104. In a particular embodiment,resolution node 806 stores a copy of the mapping table 200 so it canlook up which target correlithm object 104B corresponds to theidentified source correlithm object 104A.

By dividing each of the source correlithm objects 104A in mapping table200 into smaller portions 800 a-n, the process of determining then-dimensional distances between the input correlithm object 104 and thesource correlithm objects 104 was performed in parallel by calculationnodes 804 a-n. In other words, each calculation node 804 performed ann-dimensional distance calculation on a subset of bits of each sourcecorrelithm object 104A. Performing parallel processing, as describedabove, increases the speed and efficiency of calculating then-dimensional distances between the input correlithm object 104 and thesource correlithm objects 104A of the mapping table 200. This reducesbottlenecks in the network and in the overall correlithm objectprocessing system 810.

FIG. 8B illustrates an embodiment of correlithm object processing system810 that operates in conjunction with an actor table 310 rather than acorrelithm object mapping table 200. In this embodiment, actor table 310is configured with a first column 202 that includes target correlithmobjects 104B and a second column 204 that includes real world datavalues. The operation of system 810 in FIG. 8B is substantially similarto that of system 810 in FIG. 8A except that distribution node 802,calculation nodes 804 a-n, and resolution node 806 operate using targetcorrelithm objects 104B from actor table 310 in place of sourcecorrelithm objects 104A from actor table 200, and they operate usingreal world data values from mapping table 310 in place of targetcorrelithm objects 104B from mapping table 200.

Referring now to FIG. 9A, distribution node 902 stores a correlithmobject mapping table 200 configured with a first column 202 thatincludes source correlithm objects 104A and a second column 204 thatincludes corresponding target correlithm objects 104B. Although table200 is described with respect to columns 202 and 204, one of ordinaryskill in the art will appreciate that any suitable organization of dataor data structure that can map source correlithm objects 104A to targetcorrelithm objects 104B can be used in system 910. The source correlithmobjects 104A and target correlithm objects 104B each reside in the sameor different n-dimensional spaces 102. In one embodiment, sourcecorrelithm objects 104A and target correlithm objects 104B are eachn-bit digital words comprising binary values. For example, they maycomprise 64-bit, 128-bit, or 256-bit digital words comprising a binarystring of values.

As described above with respect to FIG. 3, a node 304 is generallyconfigured to receive an input correlithm object 104, identify thesource correlithm object 104A that most closely matches the inputcorrelithm object 104, and output the target correlithm object 104Bcorresponding to the identified source correlithm object 104A. Findingthe source correlithm object 104A that most closely matches the inputcorrelithm object 104 may involve computing the n-dimensional distance(e.g., Hamming distance, Minkowski distance, or other suitable distance)between the input correlithm object 104 and each of the sourcecorrelithm objects 104A, and identifying the source correlithm object104A that results in the smallest n-dimensional distance calculation.Performing this distance calculation serially for a relatively largenumber of source correlithm objects 104A in a relatively largecorrelithm object mapping table 200 can be time consuming, which cancreate bottlenecks in a correlithm object processing system. The presentdisclosure provides a technical solution whereby the n-dimensionaldistance calculation is performed using parallel processing techniquesimplemented by calculation nodes 904 a-n, and resolution node 906, asdescribed in greater detail below.

Distribution node 902 stores a multi-input mapping table 200 wherebyeach target correlithm object 104B corresponds to a plurality of sourcecorrelithm objects 104A. For example, in the first row of mapping table200, target correlithm object 1 corresponds to source correlithm object1-1, source correlithm object 1-2, and source correlithm object 1-n.Similarly, in the second row of mapping table 200, target correlithmobject 2 corresponds to source correlithm object 2-1, source correlithmobject 2-2, and source correlithm object 2-n. In the remainder of rowsin mapping table 200, each target correlithm object 104B corresponds tomultiple source correlithm objects 104A, as illustrated. Accordingly,the mapping table 200 includes a plurality of first source correlithmobjects 900 a, a plurality of second source correlithm objects 900 b,and a plurality of n-th source correlithm objects 900 n.

Mapping table 200 is illustrated as having three source correlithmobjects 104A corresponding to each target correlithm object 104B.However, the present disclosure contemplates any suitable number ofsource correlithm objects 104A corresponding to each target correlithmobject 104B. The number of source correlithm objects 104A thatcorrespond to each target correlithm object 104B in mapping table 200 ofFIG. 9A could be based on the computing power of the correspondingcalculation nodes 904 a, 904 b, and 904 n, and/or the speed with whichthose nodes 904 a-n can perform their processes.

Distribution node 902 transmits the plurality of first source correlithmobjects 900 a to the first calculation node 904 a, the plurality ofsecond source correlithm objects 900 b to the second calculation node904 b, and the plurality of n-th source correlithm objects 900 n to then-th calculation node 904 n. After transmission, the first calculationnode 904 a stores, or otherwise has access to, the plurality of firstsource correlithm objects 900 a; the second calculation node 904 bstores, or otherwise has access to, the plurality of second sourcecorrelithm objects 900 b; and the n-th calculation node 904 n stores, orotherwise has access to, the plurality of n-th source correlithm objects900 n. Although system 910 is primarily described as having distributionnode 902 transmit the plurality of source correlithm objects 900 a, 900b, and 900 n to corresponding calculation nodes 904 a-n, it should beunderstood that the plurality of source correlithm objects 900 a, 900 b,and 900 n can initially be stored, or otherwise accessible by,calculation nodes 904 a-n without the need for distribution node 902 totransmit them to the calculation nodes 904 a-n. Either approach achievesthe technical advantages of parallel processing, as detailed below.

In operation, each of the calculation nodes 904 a-n receives acorresponding input correlithm object 104 a-n. In particular,calculation node 904 a receives a first input correlithm object 104 a;calculation node 904 b receives a second input correlithm object 104 b;and calculation node 904 n receives an n-th input correlithm object 104n. Although FIG. 9A illustrates nodes 904 a-n receiving the inputcorrelithm objects 104 a-n from distribution node 902, nodes 904 a-n mayreceive input correlithms objects 104 a-n from any suitable sourcesincluding, but not limited to, distribution node 902. The inputcorrelithm objects 104 a-n are n-bit digital word comprising binaryvalues.

Each of the calculation nodes 904 a-n determines the n-dimensionaldistance (e.g., Hamming distance, Minkowski distance, or other suitabledistance calculation) between the relevant input correlithm object 104a-n and the relevant plurality of source correlithm objects 900 a-n. Forexample, calculation node 904 a determines the n-dimensional distancebetween the first input correlithm object 104 a and each of theplurality of first source correlithm objects 900 a. Thus, in the exampleillustrated in FIG. 9A, calculation node 904 a calculates ninen-dimensional distances, one each for the source correlithm objects 104Awithin the group of source correlithm objects 900 a. Similarly,calculation node 904 b determines the n-dimensional distance between thesecond input correlithm object 104 b and each of the plurality of secondsource correlithm objects 900 b. Thus, in the example illustrated inFIG. 9, calculation node 904 b calculates nine n-dimensional distances,one each for the source correlithm objects 104A within the group ofsource correlithm objects 900 a. Finally, calculation node 904 ndetermines the n-dimensional distance between the n-th input correlithmobject 104 n and each of the plurality of n-th source correlithm objects900 n. Thus, in the example illustrated in FIG. 9A, calculation node 904n calculates nine n-dimensional distances, one each for the sourcecorrelithm objects 104A within the group of source correlithm objects900 n. With respect to calculating a Hamming distance, as describedabove with respect to at least FIG. 1, the determined n-dimensionaldistances are based on differences between the binary valuesrepresenting the input correlithm objects 104 a-n and the binary valuesrepresenting each of the source correlithm objects 104A within thecorresponding groups of sources correlithm objects 900 a-n. Eachcalculation node 904 a-n transmits the determined n-dimensional distancevalues to the resolution node 906 for further processing, as detailedbelow.

Resolution node 906 receives the n-dimensional distance calculationsfrom each of the calculation nodes 904 a-n and adds them together foreach set of corresponding source correlithm objects 104A. For example,resolution node 906 adds together the n-dimensional distance determinedfor the source correlithm object 1-1 with the n-dimensional distancedetermined for the source correlithm object 1-2 and the n-dimensionaldistance determined for source correlithm object 1-n. Likewise,resolution node 906 adds together the n-dimensional distances togetherfor the source correlithm object 2-1, source correlithm object 2-2, andsource correlithm object 2-n. Resolution node 906 further adds togetherthe n-dimensional distances of each of the other corresponding rows ofsource correlithm objects 104A in mapping table 200. Thus, in thisparticular example, resolution node will have calculated nine separaten-dimensional distances based on these calculations, one each for eachrow of source correlithm objects 104A in mapping table 200. In aparticular embodiment, these n-dimensional distances may be representedas Hamming distances and added together as such. In other embodiments,they may be Minkowski distances, or any other suitable form ofmeasurement for n-dimensional distances.

Resolution node 906 then compares the determined n-dimensional distancesto identify the row of source correlithm objects 104A with the smallestaggregate n-dimensional distances to the input correlithm objects 104a-n. For example, if the n-dimensional distances are measured usingHamming distances, then resolution node 906 will compare the aggregateHamming distances calculated for each of the nine rows of sourcecorrelithm objects 104A in mapping table 200, to determine which row ofsource correlithm objects 104A has the smallest aggregate Hammingdistance. Resolution node 906 then identifies the target correlithmobject 104B that corresponds to the identified row of source correlithmobjects 104 with the smallest aggregate n-dimensional distance to theinput correlithm objects 104 a-n, and communicates that targetcorrelithm object 104B as output correlithm object 104. In a particularembodiment, resolution node 906 stores a copy of the mapping table 200so it can look up which target correlithm object 104B corresponds to theidentified row of source correlithm objects 104A.

In system 910, the process of determining the n-dimensional distancesbetween the input correlithm objects 104 a-n and the groups of sourcecorrelithm objects 900 a, 900 b, and 900 n is performed in parallel bycalculation nodes 904 a-n. Performing parallel processing, as describedabove, increases the speed and efficiency of calculating then-dimensional distances between the input correlithm objects 104 a-n andthe source correlithm objects 104A of the mapping table 200. Thisreduces bottlenecks in the network and in the overall correlithm objectprocessing system 910.

FIG. 9B illustrates an embodiment of correlithm object processing system910 that operates in conjunction with an actor table 310 rather than acorrelithm object mapping table 200. In this embodiment, actor table 310is configured with a first column 202 that includes target correlithmobjects 104B and a second column 204 that includes real world datavalues. The operation of system 910 in FIG. 9B is substantially similarto that of system 910 in FIG. 9A except that distribution node 902,calculation nodes 904 a-n, and resolution node 906 operate using targetcorrelithm objects 104B from actor table 310 in place of sourcecorrelithm objects 104A from mapping table 200, and they operate usingreal world data values from actor table 310 in place of targetcorrelithm objects 104B from mapping table 200.

FIG. 10 illustrates one embodiment of a flowchart 1000 implementing aprocess performed by the components of correlithm object processingsystem 810. Upon starting the process, distribution node 802 storescorrelithm object mapping table 200 at step 1002. Mapping table 200includes source correlithm objects 104A mapped to target correlithmobjects 104B. Distribution node 802 divides each source correlithmobject 104A into any suitable number of portions, such as a firstportion 800 a, a second portion 800 b, and an n-th portion 800 n, atstep 1004. Each portion comprises a subset of the binary values in thatsource correlithm object 104A. Execution proceeds to step 1006,distribution node 802 transmits the first portion 800 a to firstcalculation node 804 a; transmits the second portion 800 b to secondcalculation node 804 b; and transmits the n-th portion 800 n to n-thcalculation node 800 n.

Execution proceeds in parallel according to processing path 1008associated with first calculation node 804 a, path 1010 associated withsecond calculation node 804 b, and path 1012 associated with n-thcalculation node 804 n. Notwithstanding the ordering of discussion withrespect to the flowchart illustrated in FIG. 10, the processes describedin each of the paths 1008, 1010, and 1012 are performed substantiallysimultaneously. Furthermore, as described above, although the flowchart1000 is described as beginning with the operation of distribution node802 dividing and transmitting source correlithm objects 104A frommapping table 200 to calculation nodes 804 a-n, it should be understoodthat in at least one embodiment, the process could start with sourcecorrelithm objects 104A already divided into portions 800 a-n and storedor otherwise accessible by the appropriate calculation nodes 804 a-n.

Referring to processing path 1008, execution proceeds to step 1020 wherefirst calculation node 804 a stores first portion 800 a of each sourcecorrelithm object 104A from mapping table 200. First calculation node804 a receives input correlithm object 104 at step 1022. Executionproceeds to step 1024 where first calculation node 804 a determinesn-dimensional distances between the input correlithm object 104 and thefirst portion 800 a of each source correlithm object 104A from mappingtable 200. The determined n-dimensional distances are based ondifferences between the binary values representing the input correlithmobject 104 and the binary values representing the first portion 800 a ofeach source correlithm object 104A from mapping table 200. At step 1026,first calculation node 804 a transmits the determined n-dimensionaldistances for the first portions 800 a of the source correlithm objects104A to resolution node 806.

Referring to processing path 1010, execution proceeds to step 1030 wherefirst calculation node 804 a stores second portion 800 b of each sourcecorrelithm object 104A from mapping table 200. Second calculation node804 b receives input correlithm object 104 at step 1032. Executionproceeds to step 1034 where second calculation node 804 b determinesn-dimensional distances between the input correlithm object 104 and thesecond portion 800 b of each source correlithm object 104A from mappingtable 200. The determined n-dimensional distances are based ondifferences between the binary values representing the input correlithmobject 104 and the binary values representing the second portion 800 bof each source correlithm object 104A from mapping table 200. At step1036, second calculation node 804 b transmits the determinedn-dimensional distances for the second portions 800 b of the sourcecorrelithm objects 104A to resolution node 806.

Referring to processing path 1012, execution proceeds to step 1040 wheren-th calculation node 804 n stores n-th portion 800 n of each sourcecorrelithm object 104A from mapping table 200. N-th calculation node 804n receives input correlithm object 104 at step 1042. Execution proceedsto step 1044 where n-th calculation node 804 n determines n-dimensionaldistances between the input correlithm object 104 and the n-th portion800 n of each source correlithm object 104A from mapping table 200. Thedetermined n-dimensional distances are based on differences between thebinary values representing the input correlithm object 104 and thebinary values representing the n-th portion 800 n of each sourcecorrelithm object 104A from mapping table 200. At step 1046, n-thcalculation node 804 n transmits the determined n-dimensional distancesfor the n-th portions 800 n of the source correlithm objects 104A toresolution node 806.

Execution proceeds from paths 1008, 1010, and 1012 to step 1050 whereresolution node 806 receives the n-dimensional distances transmitted atsteps 1026, 1036, and 1046. At step 1052, for each source correlithmobject 104A, resolution node 806 adds together the n-dimensionaldistances determined for the portions 800 a-n of that source correlithmobject 104 a. Thus, for example, resolution node 806 adds together then-dimensional distance determined for the first portion 800 a of thefirst source correlithm object 104A with the n-dimensional distancedetermined for the second portion 800 b of the first source correlithmobject 104A and the n-dimensional distance determined for the n-thportion 800 n of the first source correlithm object 104A. Resolutionnode 806 also adds together the n-dimensional distance determined forthe first portion 800 a of the second source correlithm object 104A withthe n-dimensional distance determined for the second portion 800 b ofthe second source correlithm object 104A and the n-dimensional distancedetermined for the n-th portion 800 n of the second source correlithmobject 104A. Resolution node 806 conducts this process of addingtogether the n-dimensional distances for the portions 800 a-n for eachof the remainder of source correlithm objects 104A at step 1052.

Execution proceeds to step 1054, where resolution node 806 compares then-dimensional distances determined at step 1052 to each other to findthe smallest determined n-dimensional distance. At step 1056, resolutionnode 806 identifies the source correlithm object 104A corresponding tothe smallest determined n-dimensional distance. This identified sourcecorrelithm object is the closest match to the input correlithm object104 among all of the source correlithm objects 104A in the mapping table200. At step 1058, resolution node 806 then refers to the mapping table200 to identify the target correlithm object 104B associated with thesource correlithm object 104A identified at step 1056, and outputs thattarget correlithm object 104B as an output at step 1060. Executionterminates at step 1062.

FIG. 11 illustrates one embodiment of a flowchart 1100 implementing aprocess performed by the components of correlithm object processingsystem 910. Upon starting the process, distribution node 902 stores amulti-input correlithm object mapping table 200 at step 1102. Mappingtable 200 includes a plurality of first source correlithm objects 900 a,a plurality of second source correlithm objects 900 b, a plurality ofn-th source correlithm objects 900 n, and a corresponding plurality oftarget correlithm objects 104B. Each correlithm object 104 comprises ann-bit digital word of binary values. Execution proceeds to step 1104where distribution node 902 transmits the first plurality of sourcecorrelithm objects 900 a to first calculation node 904 a; transmits thesecond plurality of source correlithm objects 900 b to secondcalculation node 904 b; and transmits the n-th plurality of sourcecorrelithm objects 900 n to n-th calculation node 900 n.

Execution proceeds in parallel according to processing path 1108associated with first calculation node 904 a, path 1110 associated withsecond calculation node 904 b, and path 1112 associated with n-thcalculation node 904 n. Notwithstanding the ordering of discussion withrespect to the flowchart illustrated in FIG. 11, the processes describedin each of the paths 1108, 1110, and 1112 are performed substantiallysimultaneously. Furthermore, as described above, although the flowchart1100 is described as beginning with the operation of distribution node902 transmitting plurality of source correlithm objects 900 a-n tocalculation nodes 904 a-n, it should be understood that in at least oneembodiment, the process could start with plurality of source correlithmobjects 900 a-n already stored or otherwise accessible by theappropriate calculation nodes 904 a-n.

Referring to processing path 1108, execution proceeds to step 1120 wherefirst calculation node 904 a stores first plurality of source correlithmobjects 900 a from mapping table 200. First calculation node 904 areceives first input correlithm object 104 at step 1122. Executionproceeds to step 1124 where first calculation node 904 a determinesn-dimensional distances between the first input correlithm object 104and each of the first plurality of source correlithm objects 900 a. Thedetermined n-dimensional distances are based on differences between thebinary values representing the first input correlithm object 104 and thebinary values representing each of the first plurality of sourcecorrelithm objects 900 a from mapping table 200. At step 1126, firstcalculation node 904 a transmits the determined n-dimensional distancesfor the first plurality of source correlithm objects 900 a to resolutionnode 906.

Referring to processing path 1110, execution proceeds to step 1130 wheresecond calculation node 904 b stores second plurality of sourcecorrelithm objects 900 b from mapping table 200. Second calculation node904 b receives second input correlithm object 104 at step 1132.Execution proceeds to step 1134 where second calculation node 904 bdetermines n-dimensional distances between the second input correlithmobject 104 and each of the second plurality of source correlithm objects900 b. The determined n-dimensional distances are based on differencesbetween the binary values representing the second input correlithmobject 104 and the binary values representing each of the secondplurality of source correlithm objects 900 b from mapping table 200. Atstep 1136, second calculation node 904 b transmits the determinedn-dimensional distances for the second plurality of source correlithmobjects 900 b to resolution node 906.

Referring to processing path 1112, execution proceeds to step 1140 wheren-th calculation node 904 n stores n-th plurality of source correlithmobjects 900 n from mapping table 200. N-th calculation node 904 nreceives n-th input correlithm object 104 at step 1142. Executionproceeds to step 1144 where n-th calculation node 904 n determinesn-dimensional distances between the n-th input correlithm object 104 andeach of the n-th plurality of source correlithm objects 900 n. Thedetermined n-dimensional distances are based on differences between thebinary values representing the n-th input correlithm object 104 and thebinary values representing each of the n-th plurality of sourcecorrelithm objects 900 n from mapping table 200. At step 1146, n-thcalculation node 904 n transmits the determined n-dimensional distancesfor the n-th plurality of source correlithm objects 900 n to resolutionnode 906.

Execution proceeds from paths 1108, 1110, and 1112 to step 1150 whereresolution node 906 receives the n-dimensional distances transmitted atsteps 1126, 1136, and 1146. At step 1152, resolution node 906 addstogether the n-dimensional distances for each of the correspondingfirst, second, and n-th plurality of source correlithm objects 900 a-n.In particular, resolution node 906 adds together the determinedn-dimensional distance for each of the first plurality of sourcecorrelithm objects 900 a with the n-dimensional distances determined foreach of the corresponding second plurality of source correlithm objects900 b and with the n-dimensional distances determined for each of thecorresponding n-th plurality of source correlithm objects 900 n, suchthat each grouping of first, second, and n-th plurality of sourcecorrelithm objects 900 a-n has an aggregate n-dimensional distancecalculation. Thus, for example, the n-dimensional distances for SourceCO1-1, Source CO1-2, and Source CO 1-n are added together such that thisgrouping of source correlithm objects has an aggregate n-dimensionaldistance calculation. The same calculations are made for Source CO2-1,Source CO2-2, and Source CO2-n; and so on for each remaining group ofsource correlithm objects in mapping table 200. Execution proceeds tostep 1154, where resolution node 906 compares to each other theaggregate n-dimensional distance calculations determined at step 1152for each grouping of source correlithm objects to find the grouping withthe smallest aggregate n-dimensional distance calculation. At step 1156,resolution node 906 identifies the grouping of source correlithm objectscorresponding to the smallest aggregate n-dimensional distancecalculation. This identified grouping of source correlithm objects isthe closest match to the first, second, and n-th input correlithmobjects 104 among all of the groupings of source correlithm objects 104Ain the mapping table 200. At step 1158, resolution node 906 then refersto the mapping table 200 to identify the target correlithm object 104Bassociated with the grouping of source correlithm objects 104Aidentified at step 1156, and outputs that target correlithm object 104Bas an output at step 1160. Execution terminates at step 1162.

Referring now to FIG. 12A, distribution node 1202 stores a correlithmobject mapping table 200 configured with a first column 202 thatincludes source correlithm objects 104A and a second column 204 thatincludes corresponding target correlithm objects 104B. Although table200 is described with respect to columns 202 and 204, one of ordinaryskill in the art will appreciate that any suitable organization of dataor data structure that can map source correlithm objects 104A to targetcorrelithm objects 104B can be used in system 1210. The sourcecorrelithm objects 104A and target correlithm objects 104B each residein the same or different n-dimensional spaces 102. In one embodiment,source correlithm objects 104A and target correlithm objects 104B areeach n-bit digital words comprising binary values. For example, they maycomprise 64-bit, 128-bit, or 256-bit digital words comprising a binarystring of values.

As described above with respect to FIG. 3, a node 304 is generallyconfigured to receive an input correlithm object 104, identify thesource correlithm object 104A that most closely matches the inputcorrelithm object 104, and output the target correlithm object 104Bcorresponding to the identified source correlithm object 104A. Findingthe source correlithm object 104A that most closely matches the inputcorrelithm object 104 may involve computing the n-dimensional distance(e.g., Hamming distance, Minkowski distance, or other suitable distance)between the input correlithm object 104 and each of the sourcecorrelithm objects 104A, and identifying the source correlithm object104A that results in the smallest n-dimensional distance calculation.Performing this distance calculation serially for a relatively largenumber of source correlithm objects 104A in a relatively largecorrelithm object mapping table 200 can be time consuming, which cancreate bottlenecks in a correlithm object processing system. The presentdisclosure provides a technical solution whereby the n-dimensionaldistance calculation is performed using parallel processing techniquesimplemented by intermediate calculation nodes 1212 a-n, finalcalculation nodes 1214 a-n, and resolution node 1206, as described ingreater detail below.

Distribution node 1202 divides each source correlithm object 104A ofmapping table 200 into any suitable number of portions, such as firstportion 800 a, second portion 800 b, and n-th portion 800 n. Firstportion 800 a of a given source correlithm object 104A (e.g., sourceCO1) comprises a first subset of the n-bit digital word of binary valuesmaking up that source correlithm object 104A. For example, asillustrated in FIG. 12A, if source CO1 is a 256-bit digital word ofbinary values, then first portion 800 a of source CO1 may include thefirst 64 bits of that 256-bit digital word. Second portion 800 b of thatsource correlithm object 104A (e.g., source CO1) comprises a secondsubset of the n-bit digital word of binary values making up that sourcecorrelithm object 104A. For example, as illustrated in FIG. 12A, ifsource CO1 is a 256-bit digital word of binary values, then secondportion 800 b of source CO1 may include the second 64 bits of that256-bit digital word. N-th portion 800 n of that source correlithmobject 104A (e.g., source CO1) comprises an n-th subset of the n-bitdigital word of binary values making up that source correlithm object104A. For example, as illustrated in FIG. 12A, if source CO1 is a256-bit digital word of binary values, then n-th portion 800 n of sourceCO1 may include the last 128 bits of that 256-bit digital word. Each ofthe other source correlithm objects 104A stored in mapping table 200(e.g., source CO2, source CO3, source COa, source COb, source COc,source COx, source COy, and source COz) may be similarly divided intofirst portions 800 a, second portions 800 b, and n-th portions 800 n.

The source correlithm objects 104A are shown as being divided into threeportions of 64-bit, 64-bit, and 128-bit sizes for illustrative purposesonly. The source correlithm objects 104A may be divided into anysuitable number of portions 800, each portion 800 having any suitablenumber of bits of the digital word. For example, a 256-bit sourcecorrelithm object 104A can be divided into four portions, each having 64bits. In another non-limiting example, a 256-bit source correlithmobject 104A can be divided into four portions having 64 bits, 32 bits,128 bits, and 32 bits, respectively. In still another non-limitingexample, a 256-bit source correlithm object 104A can be divided into tenportions, with nine of those portions having 25 bits and the tenthportion having 31 bits. The present disclosure contemplates any numberof other combinations for the number of portions 800 and the size ofeach portion 800. In one embodiment, the number and sizes of portions800 that are divided from the source correlithm object 104A in mappingtable 200 are determined based on the computing power of thecorresponding intermediate calculation nodes 1212 a, 1212 b, and 1212 n,and/or the speed with which those nodes 1212 a-n can perform theirprocesses. For example, the more computing power and/or speed that aparticular node 1212 can perform its processes, the larger number ofbits that can be included in a portion 800 that is transmitted to thatnode 1212 for calculation.

Distribution node 1202 transmits the first portion 800 a of each sourcecorrelithm object 104A in mapping table 200 to the first calculationnode 1212 a, the second portion 800 b of each source correlithm object104A in mapping table 200 to the second calculation node 1212 b, and then-th portion 200 n of each source correlithm object 104A in mappingtable 200 to the n-th calculation node 1212 n. After transmission, thefirst calculation node 1212 a stores, or otherwise has access to, thefirst portions 800 a; the second calculation node 1212 b stores, orotherwise has access to, the second portions 800 a; and the n-thcalculation node 1212 n stores, or otherwise has access to, the n-thportions 800 n. Although system 1210 is primarily described as havingdistribution node 1202 divide the source correlithm objects 104A inmapping table 200 into portions 800 a-n and transmit those portions 200a-n to corresponding intermediate calculation nodes 1212 a-n, it shouldbe understood that the source correlithm objects 104A can initially becreated already divided and stored, or otherwise accessible by,intermediate calculation nodes 1212 a-n without the need fordistribution node 1202 to divide and transmit them to the intermediatecalculation nodes 1212 a-n. Either approach achieves the technicaladvantages of parallel processing, as detailed below.

Distribution node 1202 also divides correlithm object mapping table 200into any suitable number of portions, such as first portion 200 a of thecorrelithm object mapping table 200, a second portion 200 b of thecorrelithm object mapping table 200, and an n-th portion 200 n of thecorrelithm object mapping table 200. The first portion 200 a includes afirst subset of the source correlithm objects 104A and theircorresponding target correlithm objects 104B from the entire correlithmobject mapping table 200. For example, as illustrated in FIG. 12A, thefirst portion 200 a of the correlithm object mapping table 200 includessource correlithm objects 1-3 and the corresponding target correlithmobjects 1-3. The second portion 200 b includes a second subset of thesource correlithm objects 104A and their corresponding target correlithmobjects 104B from the entire correlithm object mapping table 200. Forexample, as illustrated in FIG. 12A, the second portion 200 b includessource correlithm objects a-c and the corresponding target correlithmobjects a-c. The n-th portion of the 200 n includes an n-th subset ofthe source correlithm objects 104A and their corresponding targetcorrelithm objects 104B from the entire correlithm object mapping table200. For example, as illustrated in FIG. 12A, the n-th portion 200 nincludes source correlithm objects x-z and the corresponding targetcorrelithm objects x-z. In some embodiments, the correlithm objects 104included in the first, second, and n-th portions of the mapping table200 are mutually exclusive of each other, or non-overlapping. In otherembodiments, the correlithm objects 104 included in the first, second,and n-th portions of the mapping table 200 are overlapping or at leastpartially overlapping in order to achieve redundancy in the distancecalculations between the input correlithm object 104 and the sourcecorrelithm objects 104A as detailed below.

The first, second, and n-th portions of mapping table 200 are eachillustrated as having three source and target correlithm objects forillustrative purposes only. They may have any suitable number ofcorrelithm objects 104 that may be the same or different from eachother. In one embodiment, the number of correlithm objects 104 that areincluded in the first, second, and n-th portions of the mapping table200 is determined based on the computing power of the correspondingfinal calculation nodes 1214 a, 1214 b, and 1214 n, and/or the speedwith which those nodes 1214 a-n can perform their processes. Forexample, the more computing power and/or speed that a particular node1214 can perform its processes, the larger number of correlithm objects104 can be transmitted to that node 1214 in the relevant portion of themapping table 200.

Distribution node 1202 transmits the first portion 200 a of thecorrelithm object mapping table 200 to the first final calculation node1214 a, the second portion 200 b to the second calculation node 1214 b,and the n-th portion 200 n to the n-th calculation node 1214 n. Aftertransmission, the first final calculation node 1214 a stores, orotherwise has access to, the first portion 200 a; the second calculationnode 1214 b stores, or otherwise has access to, the second portion 200b; and the n-th calculation node 1214 n stores, or otherwise has accessto, the n-th portion 200 n. Although system 1210 is primarily describedas having distribution node 1202 divide the mapping table 200 intoportions 200 a-n and transmit those portions 200 a-n to correspondingfinal calculation nodes 1214 a-n, it should be understood that themapping table 200 can initially be created in multiple portions 200 a-nand stored, or otherwise accessible by, calculation nodes 1214 a-nwithout the need to divide the mapping table 200 and transmit theportions 200 a-n by the distribution node 1202. Either approach achievesthe technical advantages of parallel processing in computing then-dimensional distances between the input correlithm object 104 and thesource correlithm objects 104A in the portions 200 a-n of the mappingtable 200.

In operation, each of the intermediate calculation nodes 1212 a-nreceives an input correlithm object 104 (or at least a portion thereof).In the example described below, the input correlithm object 104 is a256-bit digital word of binary values. Although FIG. 12A illustratesnodes 1212 a-n receiving the input correlithm object 104 fromdistribution node 1202, nodes 1212 a-n may receive input correlithmobject 104 from any suitable sources including, but not limited to,distribution node 1202. The input correlithm object 104 is an n-bitdigital word comprising binary values.

Each of the intermediate calculation nodes 1212 a-n determines then-dimensional distance (e.g., Hamming distance, Minkowski distance, orother suitable distance calculation) between the relevant bits of theinput correlithm object 104 and the relevant portions 800 a-n of thesource correlithm objects 104A. For example, intermediate calculationnode 1212 a determines the n-dimensional distance between the first 64bits of input correlithm object 104 and the first portion 800 a of eachsource correlithm object 104, which in this example is also 64 bits.Thus, in the example illustrated in FIG. 12, first intermediatecalculation node 1212 a calculates nine n-dimensional distances, oneeach for the first portions 800 a of source correlithm objects 1-3,source correlithm objects a-c, and source correlithm objects x-z.Similarly, second intermediate calculation node 1212 b determines then-dimensional distance between the next 64 bits of input correlithmobject 104 and the second portion 800 a of each source correlithm object104, which in this example is also 64 bits. Thus, in the exampleillustrated in FIG. 12A, intermediate calculation node 1212 b calculatesnine n-dimensional distances, one each for the second portions 800 b ofsource correlithm objects 1-3, source correlithm objects a-c, and sourcecorrelithm objects x-z. Finally, n-th intermediate calculation node 1212n determines the n-dimensional distance between the last 128 bits ofinput correlithm object 104 and the n-th portion 800 n of each sourcecorrelithm object 104, which in this example is also 128 bits. Thus, inthe example illustrated in FIG. 12A, intermediate calculation node 1212n calculates nine n-dimensional distances, one each for the n-thportions 800A of source correlithm objects 1-3, source correlithmobjects a-c, and source correlithm objects x-z. With respect tocalculating a Hamming distance, as described above with respect to atleast FIG. 1, the determined n-dimensional distances are based ondifferences between the binary values representing the input correlithmobject 104 and the binary values representing each of the portions 800a-n of the source correlithm objects.

Intermediate calculation nodes 1212 a-n transmit the determinedn-dimensional distance values to the final calculation nodes 1214 a-nfor further processing, as detailed below. In particular, firstintermediate calculation node 1212 a transmits the determinedn-dimensional distances associated with the first portions 800 a of thesource correlithm objects 104A from the first portion 200 a of themapping table 200 to the first final calculation node 1214 a.Intermediate calculation node 1212 a further transmits the determinedn-dimensional distances associated with the first portions 800 a of thesource correlithm objects 104A from the second portion 200 b of themapping table 200 to the second final calculation node 1214 b.Intermediate calculation node 1212 a transmits the determinedn-dimensional distances associated with the first portions 800 a of thesource correlithm objects 104A from the n-th portion 200 n of themapping table 200 to the n-th final calculation node 1214 n.

Similarly, intermediate calculation node 1212 b transmits the determinedn-dimensional distances associated with the second portions 800 b of thesource correlithm objects 104A from the first portion 200 a of themapping table 200 to the first final calculation node 1214 a.Intermediate calculation node 1212 b further transmits the determinedn-dimensional distances associated with the second portions 800 b of thesource correlithm objects 104A from the second portion 200 b of themapping table 200 to the second final calculation node 1214 b.Intermediate calculation node 1212 b transmits the determinedn-dimensional distances associated with the second portions 800 b of thesource correlithm objects 104A from the n-th portion 200 n of themapping table 200 to the n-th final calculation node 1214 n.

Furthermore, intermediate calculation node 1212 n transmits thedetermined n-dimensional distances associated with the n-th portions 800n of the source correlithm objects 104A from the first portion 200 a ofthe mapping table 200 to the first final calculation node 1214 a.Intermediate calculation node 1212 n further transmits the determinedn-dimensional distances associated with the n-th portions 800 n of thesource correlithm objects 104A from the second portion 200 b of themapping table 200 to the second final calculation node 1214 b.Intermediate calculation node 1212 n transmits the determinedn-dimensional distances associated with the n-th portions 800 n of thesource correlithm objects 104A from the n-th portion 200 n of themapping table 200 to the n-th final calculation node 1214 n.

The final calculation nodes 1214 a-n receive the n-dimensional distancecalculations described above from the various intermediate calculationnodes 1212 a-n. The final calculation nodes 1214 a-n also store, orotherwise have access to, the mapping tables 200. The first finalcalculation node 1214 a adds the n-dimensional distance calculationsreceived from each of the intermediate calculation nodes 1212 a-n foreach source correlithm object 104A in the first portion 200 a of themapping table 200. For example, first final calculation node 1214 a addstogether the n-dimensional distance determined for the first portion 800a of source correlithm object 1 with the n-dimensional distancedetermined for the second portion 800 a of source correlithm object 1and the n-dimensional distance determined for the n-th portion 800 n ofsource correlithm object 1 to determine an aggregate n-dimensionaldistance for source correlithm object 1. Likewise, first finalcalculation node 1214 a adds together the n-dimensional distances forthe first portion 800 a, second portion 800 b, and n-th portion 800 n ofsource correlithm object 2 to determine an aggregate n-dimensionaldistance for source correlithm object 2. Furthermore, first finalcalculation node 1214 a adds together the n-dimensional distances forthe first portion 800 a, second portion 800 b, and n-th portion 800 n ofsource correlithm object 3 to determine an aggregate n-dimensionaldistance for source correlithm object 3. In a particular embodiment,these n-dimensional distances may be represented as Hamming distancesand added together as such. In other embodiments, they may be Minkowskidistances, or any other suitable form of measurement for n-dimensionaldistances.

First final calculation node 1214 a then compares the determinedn-dimensional distances to identify the source correlithm object 104Afrom the first portion 200 a of the mapping table 200 with the smallestn-dimensional distance to the input correlithm object 104. For example,if the n-dimensional distances are measured using Hamming distances,then first final calculation node 1214 a will compare the aggregateHamming distances calculated for each of the three source correlithmobjects 1-3 to each other, to determine which source correlithm object104A from the first portion 200 a of the mapping table 200 has thesmallest Hamming distance. First final calculation node 1214 a thenidentifies the target correlithm object 104B that corresponds to theidentified source correlithm object 104A with the smallest n-dimensionaldistance to the input correlithm object 104, and communicates thattarget correlithm object 104B and the smallest determined n-dimensionaldistance calculation to the resolution node 1206. In a particularembodiment, first final calculation node 1214 a stores a copy of themapping table 200 so it can look up which target correlithm object 104Bcorresponds to the identified source correlithm object 104A.

The second final calculation node 1214 b adds the n-dimensional distancecalculations received from each of the intermediate calculation nodes1212 a-n for each source correlithm object 104A in the second portion200 b of the mapping table 200. For example, second final calculationnode 1214 b adds together the n-dimensional distance determined for thefirst portion 800 b of source correlithm object a with the n-dimensionaldistance determined for the second portion 800 a of source correlithmobject a and the n-dimensional distance determined for the n-th portion800 n of source correlithm object a to determine an aggregaten-dimensional distance for source correlithm object a. Likewise, secondfinal calculation node 1214 b adds together the n-dimensional distancesfor the first portion 800 a, second portion 800 b, and n-th portion 800n of source correlithm object b to determine an aggregate n-dimensionaldistance for source correlithm object b. Furthermore, second finalcalculation node 1214 b adds together the n-dimensional distances forthe first portion 800 a, second portion 800 b, and n-th portion 800 n ofsource correlithm object c to determine an aggregate n-dimensionaldistance for source correlithm object c. In a particular embodiment,these n-dimensional distances may be represented as Hamming distancesand added together as such. In other embodiments, they may be Minkowskidistances, or any other suitable form of measurement for n-dimensionaldistances.

Second final calculation node 1214 b then compares the determinedn-dimensional distances to identify the source correlithm object 104Afrom the second portion 200 b of the mapping table 200 with the smallestn-dimensional distance to the input correlithm object 104. For example,if the n-dimensional distances are measured using Hamming distances,then second final calculation node 1214 b will compare the aggregateHamming distances calculated for each of the three source correlithmobjects a-c to each other, to determine which source correlithm object104A from the second portion 200 b of the mapping table 200 has thesmallest Hamming distance. Second final calculation node 1214 b thenidentifies the target correlithm object 104B that corresponds to theidentified source correlithm object 104A with the smallest n-dimensionaldistance to the input correlithm object 104, and communicates thattarget correlithm object 104B and the smallest determined n-dimensionaldistance calculation to the resolution node 1206. In a particularembodiment, second final calculation node 1214 b stores a copy of themapping table 200 so it can look up which target correlithm object 104Bcorresponds to the identified source correlithm object 104A.

The n-th final calculation node 1214 n adds the n-dimensional distancecalculations received from each of the intermediate calculation nodes1212 a-n for each source correlithm object 104A in the n-th portion 200n of the mapping table 200. For example, n-th final calculation node1214 n adds together the n-dimensional distance determined for the firstportion 800 a of source correlithm object x with the n-dimensionaldistance determined for the second portion 800 b of source correlithmobject x and the n-dimensional distance determined for the n-th portion800 n of source correlithm object x to determine an aggregaten-dimensional distance for source correlithm object x. Likewise, n-thfinal calculation node 1214 n adds together the n-dimensional distancesfor the first portion 800 a, second portion 800 b, and n-th portion 800n of source correlithm object y to determine an aggregate n-dimensionaldistance for source correlithm object y. Furthermore, n-th finalcalculation node 1214 n adds together the n-dimensional distances forthe first portion 800 a, second portion 800 b, and n-th portion 800 n ofsource correlithm object z to determine an aggregate n-dimensionaldistance for source correlithm object z. In a particular embodiment,these n-dimensional distances may be represented as Hamming distancesand added together as such. In other embodiments, they may be Minkowskidistances, or any other suitable form of measurement for n-dimensionaldistances.

N-th final calculation node 1214 n then compares the determinedn-dimensional distances to identify the source correlithm object 104Afrom the n-th portion 200 n of the mapping table 200 with the smallestn-dimensional distance to the input correlithm object 104. For example,if the n-dimensional distances are measured using Hamming distances,then n-th final calculation node 1214 n will compare the aggregateHamming distances calculated for each of the three source correlithmobjects x-z to each other, to determine which source correlithm object104A from the n-th portion 200 n of the mapping table 200 has thesmallest Hamming distance. N-th final calculation node 1214 n thenidentifies the target correlithm object 104B that corresponds to theidentified source correlithm object 104A with the smallest n-dimensionaldistance to the input correlithm object 104, and communicates thattarget correlithm object 104B and the smallest determined n-dimensionaldistance calculation to the resolution node 1206. In a particularembodiment, n-th final calculation node 1214 n stores a copy of themapping table 200 so it can look up which target correlithm object 104Bcorresponds to the identified source correlithm object 104A.

Resolution node 1206 then compares the n-dimensional distances receivedfrom the final calculation nodes 1214 a-n with each other to identifythe smallest n-dimensional distance. Resolution node 1206 thenidentifies the target correlithm object 104B associated with thesmallest determined n-dimensional distance and outputs the identifiedtarget correlithm object 104B as output correlithm object 104.

By dividing the source correlithm objects 104A in mapping table 200 intosmaller portions 800 a-n, and by further dividing the mapping table 200into smaller portions 200 a-b, the process of determining then-dimensional distances between the input correlithm object 104 and thesource correlithm objects 104 is performed in parallel by intermediatecalculation nodes 1212 a-n and final calculation nodes 1214 a.Performing parallel processing, as described above, increases the speedand efficiency of calculating the n-dimensional distances between theinput correlithm object 104 and the source correlithm objects 104A ofthe mapping table 200. This reduces bottlenecks in the network and inthe overall correlithm object processing system 1210.

Although system 1210 is described with respect to dividing each sourcecorrelithm object into portions 800 a-n and dividing mapping table 200into portions 200 a-n, it should be understood that system 1210 can beimplemented using a multi-input mapping table 200 having multiple sourcecorrelithm objects 104A corresponding to each target correlithm object104B, as described in FIG. 9A. In this embodiment, intermediatecalculation nodes 1212 a-n operate on plurality of source correlithmobjects 900 a-n (as explained in conjunction with FIG. 9A) and inputcorrelithm objects 104 a-n rather than on portions of a given sourcecorrelithm object 800 a-n and corresponding portions of an inputcorrelithm object 104. The mapping table 200 is still divided intoportions 200 a-n and the final calculation nodes 1214 a-n still operateon those portions 200 a-n, as described above for system 1210.

FIG. 12B illustrates an embodiment of correlithm object processingsystem 1210 that operates in conjunction with an actor table 310 ratherthan a correlithm object mapping table 200. In this embodiment, actortable 310 is configured with a first column 202 that includes targetcorrelithm objects 104B and a second column 204 that includes real worlddata values. The operation of system 1210 in FIG. 12B is substantiallysimilar to that of system 1210 in FIG. 12A except that distribution node1202, intermediate calculation nodes 1212 a-n, final calculation nodes1214 a-n, and resolution node 1206 operate using target correlithmobjects 104B from actor table 310 in place of source correlithm objects104A from mapping table 200, and they operate using real world datavalues from actor table 310 in place of target correlithm objects 104Bfrom mapping table 200.

FIGS. 13A-B illustrate one embodiment of a flowchart 1300 implementing aprocess performed by the components of correlithm object processingsystem 1210. Upon starting the process, distribution node 1202 storescorrelithm object mapping table 200 at step 1302. Mapping table 200includes source correlithm objects 104A mapped to target correlithmobjects 104B. Distribution node 1202 divides each source correlithmobject 104A into any suitable number of portions, such as first portion800 a, second portion 800 b, and n-th portion 800 n, at step 1304. Eachportion 800 a-n comprises a subset of the binary values in that sourcecorrelithm object 104A. Execution proceeds to step 1306, wheredistribution node 1202 transmits the first portion 800 a to firstintermediate calculation node 1212 a; transmits the second portion 800 bto second intermediate calculation node 1212 b; and transmits the n-thportion 800 n to n-th intermediate calculation node 1212 n. At step1308, distribution node 1202 divides mapping table 200 into any suitablenumber of portions, such as a first portion 200 a, a second portion 200b, and an n-th portion 200 n. Each portion 200 a-n comprises a subset ofthe binary values in that source correlithm object 104A. Executionproceeds to step 1310, where distribution node 1202 transmits the firstportion 200 a to first final calculation node 1214 a; transmits thesecond portion 200 b to second final calculation node 1214 b; andtransmits the n-th portion 200 n to n-th final calculation node 1214 n.

Execution proceeds in parallel according to processing path 1312associated with first intermediate calculation node 1212 a, path 1314associated with second intermediate calculation node 1212 b, and path1316 associated with n-th intermediate calculation node 1212 n.Notwithstanding the ordering of discussion with respect to the flowchartillustrated in FIGS. 13A-B, the processes described in each of the paths1312, 1314, and 1316 are performed substantially simultaneously.

Referring to processing path 1312, execution proceeds to step 1320 wherefirst intermediate calculation node 1212 a stores first portion 800 a ofeach source correlithm object 104A from mapping table 200. Firstintermediate calculation node 1212 a receives input correlithm object104 at step 1322. Execution proceeds to step 1324 where firstintermediate calculation node 1212 a determines n-dimensional distancesbetween the input correlithm object 104 and the first portion 800 a ofeach source correlithm object 104A from mapping table 200. Thedetermined n-dimensional distances are based on differences between thebinary values representing the input correlithm object 104 and thebinary values representing the first portion 800 a of each sourcecorrelithm object 104A from mapping table 200. At step 1326, firstintermediate calculation node 1212 a transmits to first finalcalculation node 1214 a the determined n-dimensional distances for thefirst portions 800 a of the source correlithm objects 104A from thefirst portion 200 a of mapping table 200. At step 1327, firstintermediate calculation node 1212 a transmits to second finalcalculation node 1214 b the determined n-dimensional distances for thefirst portions 800 a of the source correlithm objects 104A from thesecond portion 200 b of mapping table 200. At step 1328, firstintermediate calculation node 1212 a transmits to n-th final calculationnode 1214 n the determined n-dimensional distances for the firstportions 800 a of the source correlithm objects 104A from the n-thportion 200 n of mapping table 200.

Referring to processing path 1314, execution proceeds to step 1330 wheresecond intermediate calculation node 1212 b stores second portion 800 bof each source correlithm object 104A from mapping table 200. Secondintermediate calculation node 1212 b receives input correlithm object104 at step 1332. Execution proceeds to step 1334 where secondintermediate calculation node 1212 b determines n-dimensional distancesbetween the input correlithm object 104 and the second portion 800 b ofeach source correlithm object 104A from mapping table 200. Thedetermined n-dimensional distances are based on differences between thebinary values representing the input correlithm object 104 and thebinary values representing the second portion 800 b of each sourcecorrelithm object 104A from mapping table 200. At step 1336, secondintermediate calculation node 1212 b transmits to first finalcalculation node 1214 a the determined n-dimensional distances for thesecond portions 800 b of the source correlithm objects 104A from thefirst portion 200 a of mapping table 200. At step 1337, secondintermediate calculation node 1212 a transmits to second finalcalculation node 1214 b the determined n-dimensional distances for thesecond portions 800 b of the source correlithm objects 104A from thesecond portion 200 b of mapping table 200. At step 1338, secondintermediate calculation node 1212 b transmits to n-th final calculationnode 1214 n the determined n-dimensional distances for the secondportions 800 b of the source correlithm objects 104A from the n-thportion 200 n of mapping table 200.

Referring to processing path 1316, execution proceeds to step 1340 wheren-th intermediate calculation node 1212 n stores n-th portion 800 n ofeach source correlithm object 104A from mapping table 200. N-thintermediate calculation node 1212 n receives input correlithm object104 at step 1342. Execution proceeds to step 1344 where n-thintermediate calculation node 1212 n determines n-dimensional distancesbetween the input correlithm object 104 and the n-th portion 800 n ofeach source correlithm object 104A from mapping table 200. Thedetermined n-dimensional distances are based on differences between thebinary values representing the input correlithm object 104 and thebinary values representing the n-th portion 800 n of each sourcecorrelithm object 104A from mapping table 200. At step 1346, n-thintermediate calculation node 1212 n transmits to first finalcalculation node 1214 a the determined n-dimensional distances for then-th portions 800 n of the source correlithm objects 104A from the firstportion 200 a of mapping table 200. At step 1347, n-th intermediatecalculation node 1212 n transmits to second final calculation node 1214b the determined n-dimensional distances for the n-th portions 800 n ofthe source correlithm objects 104A from the second portion 200 b ofmapping table 200. At step 1348, n-th intermediate calculation node 1212n transmits to n-th final calculation node 1214 n the determinedn-dimensional distances for the n-th portions 800 n of the sourcecorrelithm objects 104A from the n-th portion 200 n of mapping table200.

Execution proceeds in parallel from paths 1312, 1314, and 1316 to path1352 associated with first final calculation node 1214 a, path 1354associated with second final calculation node 1214 b, and path 1356associated with n-th final calculation node 1214 n. Notwithstanding theordering of discussion with respect to the flowchart illustrated inFIGS. 13A-B, the processes described in each of the paths 1352, 1354,and 1356 are performed substantially simultaneously.

Referring to path 1352, execution proceeds to step 1360 where firstfinal calculation node 1214 a stores (or otherwise has access to)mapping table 200. At step 1362, for each source correlithm object 104Ain the first portion 200 a of the mapping table 200, first finalcalculation node 1214 a adds together the n-dimensional distancesdetermined for the portions 800 a-n of that source correlithm object 104a. Thus, for example, first final calculation node 1214 a adds togetherthe n-dimensional distance determined for the first portion 800 a ofsource correlithm object 1 with the n-dimensional distance determinedfor the second portion 800 b of the source correlithm object 1 and then-dimensional distance determined for the n-th portion 800 n of thesource correlithm object 1. First final calculation node 1214 a alsoadds together the n-dimensional distance determined for the firstportion 800 a of the source correlithm object 2 with the n-dimensionaldistance determined for the second portion 800 b of the sourcecorrelithm object 2 and the n-dimensional distance determined for then-th portion 800 n of the source correlithm object 2. First finalcalculation node 1214 a also adds together the n-dimensional distancedetermined for the first portion 800 a of the source correlithm object 3with the n-dimensional distance determined for the second portion 800 bof the source correlithm object 3 and the n-dimensional distancedetermined for the n-th portion 800 n of the source correlithm object 3.

Execution proceeds to step 1364, where first final calculation node 1214a compares the n-dimensional distances determined at step 1362 to eachother to find the smallest determined n-dimensional distance. At step1366, first final calculation node 1214 a identifies the sourcecorrelithm object 104A corresponding to the smallest n-dimensionaldistance determined at step 1364. This identified source correlithmobject is the closest match to the input correlithm object 104 among allof the source correlithm objects 104A in the first portion 200 a ofmapping table 200. At step 1368, first final calculation node 1214 athen refers to the mapping table 200 to identify the target correlithmobject 104B associated with the source correlithm object 104A identifiedat step 1366. At step 1369, first final calculation node 1214 atransmits to resolution node 1306 the target correlithm object 104Bidentified at step 1368 and the smallest n-dimensional distancedetermined at step 1364.

Referring to path 1354, execution proceeds to step 1370 where secondfinal calculation node 1214 b stores (or otherwise has access to)mapping table 200. At step 1372, for each source correlithm object 104Ain the second portion 200 b of the mapping table 200, second finalcalculation node 1214 b adds together the n-dimensional distancesdetermined for the portions 800 a-n of that source correlithm object 104a. Thus, for example, second final calculation node 1214 b adds togetherthe n-dimensional distance determined for the first portion 800 a ofsource correlithm object a with the n-dimensional distance determinedfor the second portion 800 b of the source correlithm object a and then-dimensional distance determined for the n-th portion 800 n of thesource correlithm object a. Second final calculation node 1214 b alsoadds together the n-dimensional distance determined for the firstportion 800 a of the source correlithm object b with the n-dimensionaldistance determined for the second portion 800 b of the sourcecorrelithm object b and the n-dimensional distance determined for then-th portion 800 n of the source correlithm object b. Second finalcalculation node 1214 b also adds together the n-dimensional distancedetermined for the first portion 800 a of the source correlithm object cwith the n-dimensional distance determined for the second portion 800 bof the source correlithm object c and the n-dimensional distancedetermined for the n-th portion 800 n of the source correlithm object c.

Execution proceeds to step 1374, where second final calculation node1214 b compares the n-dimensional distances determined at step 1372 toeach other to find the smallest determined n-dimensional distance. Atstep 1376, second final calculation node 1214 b identifies the sourcecorrelithm object 104A corresponding to the smallest n-dimensionaldistance determined at step 1374. This identified source correlithmobject is the closest match to the input correlithm object 104 among allof the source correlithm objects 104A in the second portion 200 b ofmapping table 200. At step 1378, second final calculation node 1214 bthen refers to the mapping table 200 to identify the target correlithmobject 104B associated with the source correlithm object 104A identifiedat step 1376. At step 1379, second final calculation node 1214 btransmits to resolution node 1306 the target correlithm object 104Bidentified at step 1378 and the smallest n-dimensional distancedetermined at step 1374.

Referring to path 1356, execution proceeds to step 1380 where n-th finalcalculation node 1214 n stores (or otherwise has access to) mappingtable 200. At step 1382, for each source correlithm object 104A in then-th portion 200 n of the mapping table 200, n-th final calculation node1214 n adds together the n-dimensional distances determined for theportions 800 a-n of that source correlithm object 104 a. Thus, forexample, n-th final calculation node 1214 n adds together then-dimensional distance determined for the first portion 800 a of sourcecorrelithm object x with the n-dimensional distance determined for thesecond portion 800 b of the source correlithm object x and then-dimensional distance determined for the n-th portion 800 n of thesource correlithm object x. N-th final calculation node 1214 n also addstogether the n-dimensional distance determined for the first portion 800a of the source correlithm object y with the n-dimensional distancedetermined for the second portion 800 b of the source correlithm objecty and the n-dimensional distance determined for the n-th portion 800 nof the source correlithm object y. N-th final calculation node 1214 nalso adds together the n-dimensional distance determined for the firstportion 800 a of the source correlithm object z with the n-dimensionaldistance determined for the second portion 800 b of the sourcecorrelithm object z and the n-dimensional distance determined for then-th portion 800 n of the source correlithm object z.

Execution proceeds to step 1384, where n-th final calculation node 1214n compares the n-dimensional distances determined at step 1382 to eachother to find the smallest determined n-dimensional distance. At step1386, n-th final calculation node 1214 n identifies the sourcecorrelithm object 104A corresponding to the smallest n-dimensionaldistance determined at step 1384. This identified source correlithmobject is the closest match to the input correlithm object 104 among allof the source correlithm objects 104A in the n-th portion 200 n ofmapping table 200. At step 1388, n-th final calculation node 1214 n thenrefers to the mapping table 200 to identify the target correlithm object104B associated with the source correlithm object 104A identified atstep 1386. At step 1389, n-th final calculation node 1214 n transmits toresolution node 1306 the target correlithm object 104B identified atstep 1388 and the smallest n-dimensional distance determined at step1384.

Execution proceeds from paths 1352, 1354, and 1356 to step 1390, whereresolution node 1306 receives the target correlithm objects 104B andn-dimensional distances communicated by the final calculation nodes 1214a-n. At step 1392, resolution node 1306 compares the n-dimensionaldistances transmitted by the first final calculation node 1214 a, thesecond final calculation node 1214 b, and the n-th calculation node 1214n to each other. At step 1394, resolution node 1206 identifies thesmallest n-dimensional distance based on the comparison performed atstep 1392. Execution proceeds to step 1396, where resolution node 1206identifies the target correlithm object 104B associated with thesmallest n-dimensional distance determined at step 1394. At step 1398,resolution node 1206 outputs the target correlithm object 104Bidentified at step 1396. Execution terminates at step 1399.

While several embodiments have been provided in the present disclosure,it should be understood that the disclosed systems and methods might beembodied in many other specific forms without departing from the spiritor scope of the present disclosure. The present examples are to beconsidered as illustrative and not restrictive, and the intention is notto be limited to the details given herein. For example, the variouselements or components may be combined or integrated in another systemor certain features may be omitted, or not implemented.

In addition, techniques, systems, subsystems, and methods described andillustrated in the various embodiments as discrete or separate may becombined or integrated with other systems, modules, techniques, ormethods without departing from the scope of the present disclosure.Other items shown or discussed as coupled or directly coupled orcommunicating with each other may be indirectly coupled or communicatingthrough some interface, device, or intermediate component whetherelectrically, mechanically, or otherwise. Other examples of changes,substitutions, and alterations are ascertainable by one skilled in theart and could be made without departing from the spirit and scopedisclosed herein.

To aid the Patent Office, and any readers of any patent issued on thisapplication in interpreting the claims appended hereto, applicants notethat they do not intend any of the appended claims to invoke 35 U.S.C. §112(f) as it exists on the date of filing hereof unless the words “meansfor” or “step for” are explicitly used in the particular claim.

1. A distributed node network to emulate a correlithm object processingsystem, comprising: a resolution node; a first calculation nodecommunicatively coupled to the resolution node and configured to store afirst portion of each source correlithm object of a mapping table,wherein: the mapping table comprises a plurality of source correlithmobjects and a corresponding plurality of target correlithm objects; eachsource correlithm object comprises an n-bit digital word of binaryvalues; and each target correlithm object comprises an n-bit digitalword of binary values; the first portion of each source correlithmobject comprises a first subset of the binary values in that sourcecorrelithm object; the first calculation node is further configured to:receive at least a portion of an input correlithm object that comprisesan n-bit digital word of binary values; determine n-dimensionaldistances between the input correlithm object and the first portion ofeach source correlithm object; transmit the determined n-dimensionaldistances associated with the source correlithm objects to theresolution node; a second calculation node communicatively coupled tothe resolution node and configured to store a second portion of eachsource correlithm object of the mapping table, wherein: the secondportion of each source correlithm object comprises a second subset ofthe binary values in that source correlithm object; and the secondcalculation node is configured to: receive at least a portion of theinput correlithm object; determine n-dimensional distances between theinput correlithm object and the second portion of each source correlithmobject; transmit the determined n-dimensional distances associated withthe source correlithm objects to the resolution node; and wherein theresolution node is configured to: add the n-dimensional distancedetermined for the first portion of each source correlithm object withthe n-dimensional distance determined for the second portion of eachsource correlithm object; compare the determined n-dimensional distancesfor the source correlithm objects to each other to identify the sourcecorrelithm object with the smallest n-dimensional distance to the inputcorrelithm object; identify the target correlithm object thatcorresponds to the identified source correlithm object; and output theidentified target correlithm object.
 2. The distributed node network ofclaim 1, further comprising an n-th calculation node communicativelycoupled to the resolution node and configured to store an n-th portionof each source correlithm object of the mapping table, wherein: the n-thportion of each source correlithm object comprises an n-th subset of thebinary values in that source correlithm object; and the n-th calculationnode is configured to: receive at least a portion of the inputcorrelithm object; determine n-dimensional distances between the inputcorrelithm object and the n-th portion of each source correlithm object;and transmit the determined n-dimensional distances associated with thesource correlithm objects to the resolution node; wherein adding by theresolution node comprises adding the n-dimensional distance determinedfor the first portion of each source correlithm object with then-dimensional distance determined for the second portion of each sourcecorrelithm object and the n-dimensional distance determined for the n-thportion of each source correlithm object.
 3. The distributed nodenetwork of claim 1, wherein: the n-dimensional distances determined bythe first calculation node and the second calculation node compriseHamming distances; and the smallest determined n-dimensional distanceidentified by the resolution node comprises a Hamming distance.
 4. Thedistributed node network of claim 1, wherein the second portion of eachsource correlithm object is different than the first portion of eachsource correlithm object.
 5. The distribution node network of claim 1,further comprising a distribution node configured to: divide each sourcecorrelithm object of the mapping table into at least the first portionthat comprises the first subset of binary values in that sourcecorrelithm object and the second portion that comprises the secondsubset of the n binary values in that source correlithm object; transmitthe first portion of each source correlithm object to the firstcalculation node; and transmit the second portion of each sourcecorrelithm object to the second calculation node.
 6. The distributionnode network of claim 1, wherein the first calculation node and thesecond calculation node execute substantially simultaneously on separateprocessors.
 7. The distribution node network of claim 1, wherein thefirst and second calculation nodes implement parallel processing toreduce network latency.
 8. A method for emulating a correlithm objectprocessing system in a distribution node network, comprising: storing afirst portion of each source correlithm object of a mapping table at afirst calculation node, wherein: the mapping table comprises a pluralityof source correlithm objects and a corresponding plurality of targetcorrelithm objects; each source correlithm object comprises an n-bitdigital word of binary values; and each target correlithm objectcomprises an n-bit digital word of binary values; the first portion ofeach source correlithm object comprises a first subset of the binaryvalues in that source correlithm object; receiving at the firstcalculation node at least a portion of an input correlithm object thatcomprises an n-bit digital word of binary values; determiningn-dimensional distances between the input correlithm object and thefirst portion of each source correlithm object, wherein the determinedn-dimensional distances are based on differences between the binaryvalues representing the input correlithm object and the binary valuesrepresenting the first portion of each source correlithm object;transmitting the determined n-dimensional distances associated with thesource correlithm objects to a resolution node; storing a second portionof each source correlithm object of the mapping table at a secondcalculation node, wherein the second portion of each source correlithmobject comprises a second subset of the binary values in that sourcecorrelithm object; receiving at the second calculation node at least aportion of the input correlithm object; determining n-dimensionaldistances between the input correlithm object and the second portion ofeach source correlithm object, wherein the determined n-dimensionaldistances are based on differences between the binary valuesrepresenting the input correlithm object and the binary valuesrepresenting the second portion of each source correlithm object;transmitting the determined n-dimensional distances associated with thesource correlithm objects to the resolution node; receiving at theresolution node the n-dimensional distances determined by the firstcalculation node and the second calculation node; adding then-dimensional distance determined for the first portion of each sourcecorrelithm object with the n-dimensional distance determined for thesecond portion of each source correlithm object; comparing thedetermined n-dimensional distances to identify the source correlithmobject with the smallest n-dimensional distance to the input correlithmobject; identifying the target correlithm object associated with theidentified source correlithm object; and outputting the identifiedtarget correlithm object.
 9. The method of claim 8, further comprising:storing an n-th portion of each source correlithm object of the mappingtable at an n-th calculation node, wherein the n-th portion of eachsource correlithm object comprises an n-th subset of the binary valuesin that source correlithm object; receiving at the n-th calculation nodeat least a portion of the input correlithm object; determiningn-dimensional distances between the input correlithm object and the n-thportion of each source correlithm object, wherein the determinedn-dimensional distances are based on differences between the binaryvalues representing the input correlithm object and the binary valuesrepresenting the n-th portion of each source correlithm object; andtransmitting the determined n-dimensional distances associated with thesource correlithm objects to the resolution node; wherein adding then-dimensional distances comprises adding the n-dimensional distancedetermined for the first portion of each source correlithm object withthe n-dimensional distance determined for the second portion of eachsource correlithm object and the n-dimensional distance determined forthe n-th portion of each source correlithm object.
 10. The method ofclaim 8, wherein: the n-dimensional distances determined by the firstcalculation node and the second calculation node comprise Hammingdistances; and the smallest determined n-dimensional distance identifiedby the resolution node comprises a Hamming distance.
 11. The method ofclaim 8, wherein the second portion of each source correlithm object isdifferent than the first portion of each source correlithm object. 12.The method of claim 8, further comprising: dividing at a distributionnode each source correlithm object of the mapping table into at leastthe first portion that comprises the first subset of the binary valuesin that source correlithm object and the second portion that comprisesthe second subset of the binary values in that source correlithm object;transmitting the first portion of each source correlithm object to thefirst calculation node; and transmitting the second portion of eachsource correlithm object to the second calculation node.
 13. The methodof claim 8, wherein the first calculation node and the secondcalculation node execute substantially simultaneously on separateprocessors.
 14. The method of claim 8, wherein the determinedn-dimensional distances are based on differences between the binaryvalues representing the input correlithm object and the binary valuesrepresenting a particular portion of each source correlithm object. 15.A distributed node network to emulate a correlithm object processingsystem, comprising: a resolution node; a first calculation nodecommunicatively coupled to the resolution node and configured to store afirst portion of each source correlithm object of a mapping table,wherein: the mapping table comprises a plurality of source correlithmobjects and a corresponding plurality of target correlithm objects; eachsource correlithm object comprises an n-bit digital word of binaryvalues; and each target correlithm object comprises an n-bit digitalword of binary values; the first portion of each source correlithmobject comprises a first subset of the binary values in that sourcecorrelithm object; the first calculation node is further configured to:receive at least a portion of an input correlithm object that comprisesan n-bit digital word of binary values; determine Hamming distancesbetween the input correlithm object and the first portion of each sourcecorrelithm object, wherein the determined Hamming distances are based ondifferences between the binary values representing the input correlithmobject and the binary values representing the first portion of eachsource correlithm object; transmit the determined Hamming distancesassociated with the source correlithm objects to the resolution node; asecond calculation node communicatively coupled to the resolution nodeand configured to store a second portion of each source correlithmobject of the mapping table, wherein: the second portion of each sourcecorrelithm object comprises a second subset of the binary values in thatsource correlithm object; and the second calculation node is configuredto: receive at least a portion of the input correlithm object; determineHamming distances between the input correlithm object and the secondportion of each source correlithm object, wherein the determined Hammingdistances are based on differences between the binary valuesrepresenting the input correlithm object and the binary valuesrepresenting the second portion of each source correlithm object;transmit the determined n-dimensional distances associated with thesource correlithm objects to the resolution node; and wherein theresolution node is configured to: add the Hamming distance determinedfor the first portion of each source correlithm object with the Hammingdistance determined for the second portion of each source correlithmobject; compare the determined Hamming distances for the sourcecorrelithm objects to each other to identify the source correlithmobject with the smallest Hamming distance to the input correlithmobject; identify the target correlithm object that corresponds to theidentified source correlithm object; and output the identified targetcorrelithm object.
 16. The distributed node network of claim 15, furthercomprising an n-th calculation node communicatively coupled to theresolution node and configured to store an n-th portion of each sourcecorrelithm object of the mapping table, wherein: the n-th portion ofeach source correlithm object comprises an n-th subset of the binaryvalues in that source correlithm object; and the n-th calculation nodeis configured to: receive at least a portion of the input correlithmobject; determine Hamming distances between the input correlithm objectand the n-th portion of each source correlithm object; and transmit thedetermined Hamming distances associated with the source correlithmobjects to the resolution node; wherein adding by the resolution nodecomprises adding the Hamming distance determined for the first portionof each source correlithm object with the Hamming distance determinedfor the second portion of each source correlithm object and the Hammingdistance determined for the n-th portion of each source correlithmobject.
 17. The distributed node network of claim 15, wherein the secondportion of each source correlithm object is different than the firstportion of each source correlithm object.
 18. The distribution nodenetwork of claim 15, further comprising a distribution node configuredto: divide each source correlithm object of the mapping table into atleast the first portion that comprises the first subset of binary valuesin that source correlithm object and the second portion that comprisesthe second subset of the binary values in that source correlithm object;transmit the first portion of each source correlithm object to the firstcalculation node; and transmit the second portion of each sourcecorrelithm object to the second calculation node.
 19. The distributionnode network of claim 15, wherein the first calculation node and thesecond calculation node execute substantially simultaneously on separateprocessors.
 20. The distribution node network of claim 15, wherein thefirst and second calculation nodes implement parallel processing toreduce network latency.